Public Cloud Encyclopedia: AI and ML Products

Artificial intelligence (AI) is the application of rapid data processing, machine learning, predictive analysis, and automation to simulate intelligent behavior and problem solving capabilities with machines and software.

It is intelligence of machines and computer programs, versus natural intelligence, which is intelligence of humans and animals. Machines and programs that use artificial intelligence are typically designed to read and interpret a data input and then respond to it by using predictive analytics or machine learning.

Machine learning (ML) and artificial intelligence are very related and often confused as being one and the same. In short, machine learning is the science and approach that enables the creation of artificially intelligent machines and programs.

Artificial intelligence frequently uses machine learning techniques to improve the system's ability to "learn" and better interpret and react to inputs. Many in the field only consider a system to be "intelligent" when it uses machine learning to learn and improve.
(DeepAI.org)

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AI Optimized Compute Services

AI Hardware Accelerators (3)

Optimized Hardware Configurations (6)

Pre-Configured Virtual Machines and Containers (10)

Machine Learning as a Service (MLaaS)

Platforms (6)

Automated Machine Learning (AutoML) (2)

Annotators (2)

Cognitive Services

Vision (9)

Text-to-Speech (3)

Speech-to-Text (4)

Image-to-Text (1)

Computational Linguistics (10)

AI-Powered Application Services

AI Apps and Agents (Bots) (3)

AI-Powered Personalization Services (3)

AI-Powered Anomaly and Fraud Detector Services (2)

AI-Powered Forecast Services (1)

AI-Powered Software Intelligence (1)

AI-Powered Search Services (10)

AI Optimized Compute Services

Specialized hardware and software resources designed to accelerate and optimize artificial intelligence applications (e.g. artificial neural networks, machine vision and machine learning).

Specialized hardware and software resources designed to accelerate and optimize artificial intelligence applications, especially artificial neural networks, machine vision and machine learning.

AI Hardware Accelerators (3)

Specialized hardware accelerator designed to accelerate artificial intelligence applications, especially artificial neural networks, machine vision and machine learning.

An AI accelerator is a class of specialized hardware accelerator designed to accelerate artificial intelligence applications, especially artificial neural networks, machine vision and machine learning. Typical applications include algorithms for robotics, internet of things and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2018, a typical AI integrated circuit chip contains billions of MOSFET transistors.

A number of vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design.

Links:

• https://en.wikipedia.org/wiki/AI_accelerator

• [AWS] Inferentia
  High performance machine learning inference chip, custom designed by AWS.
  The demand for deep learning acceleration is growing at a rapid pace, and across a wide range of applications. Applications such as personalized search recommendations, dynamic pricing, or automated customer support, are growing in sophistication and becoming more expensive to run in production. As more applications embed machine learning capabilities, a higher percentage of workloads needs acceleration, including ones that need low-latency, real-time performance. These applications benefit from infrastructure optimized to execute machine learning algorithms. AWS’s vision is to make deep learning pervasive for everyday developers and to democratize access to cutting edge hardware made available in a low-cost pay-as-you-go usage model. AWS Inferentia is a big step and commitment that will help us deliver on this vision. AWS Inferentia is designed to provide high inference performance in the cloud, drive down the total cost of inference, and to make it easy for you to integrate machine learning as part of your standard application features and capabilities.

  

  Source: https://d1.awsstatic.com/re19/Inferentia-image.ff8c860375e648fb9f60b27f3125fc21adda3a59.JPG

  Links:

  • aws.amazon.com/machine-learning/inferentia
• [Google Cloud] TPU
  An AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning, particularly using Google's own TensorFlow software.
  A tensor processing unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning, particularly using Google's own TensorFlow software. Google began using TPUs internally in 2015, and in 2018 made them available for third party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale.

  

  Source: https://cloud.google.com/images/products/tpu/google-cloud-ai_2x.png

  

  Source: https://cloud.google.com/images/products/tpu/machine-learning-performance_2x.png

  

  Source: https://upload.wikimedia.org/wikipedia/commons/thumb/b/be/Tensor_Processing_Unit_3.0.jpg/440px-Tensor_Processing_Unit_3.0.jpg

  Links:

  • cloud.google.com/tpu
  • en.wikipedia.org/wiki/Tensor_processing_unit
• [Azure] Graphcore
  Graphcore collaboration with Microsoft, Graphcore® Intelligence Processing Units (IPUs) on Microsoft Azure.
  

  Source: https://www.graphcore.ai/hubfs/P1-chassis01.jpg

  Links:

  • graphcore.ai/posts/microsoft-and-graphcore-collaborate-to-accelerate-artificial-intelligence

Optimized Hardware Configurations (6)

Specialized computer system configurations designed to accelerate artificial intelligence applications.

Specialized computer systems designed to accelerate artificial intelligence applications.

• [AWS] EC2 P3 Instances
  High performance compute in the cloud with up to 8 NVIDIA® V100 Tensor Core GPUs and up to 100 Gbps of networking throughput.
  Amazon EC2 P3 instances deliver high performance compute in the cloud with up to 8 NVIDIA® V100 Tensor Core GPUs and up to 100 Gbps of networking throughput for machine learning and HPC applications. These instances deliver up to one petaflop of mixed-precision performance per instance to significantly accelerate machine learning and high performance computing applications. Amazon EC2 P3 instances have been proven to reduce machine learning training times from days to minutes, as well as increase the number of simulations completed for high performance computing by 3-4x.
  
  With up to 4x the network bandwidth of P3.16xlarge instances, Amazon EC2 P3dn.24xlarge instances are the latest addition to the P3 family, optimized for distributed machine learning and HPC applications. These instances provide up to 100 Gbps of networking throughput, 96 custom Intel® Xeon® Scalable (Skylake) vCPUs, 8 NVIDIA® V100 Tensor Core GPUs with 32 GB of memory each, and 1.8 TB of local NVMe-based SSD storage. P3dn.24xlarge instances also support Elastic Fabric Adapter (EFA) which accelerates distributed machine learning applications that use NVIDIA Collective Communications Library (NCCL). EFA can scale to thousands of GPUs, significantly improving the throughput and scalability of deep learning training models, which leads to faster results.

  

  Source: https://d1.awsstatic.com/4667-P3Instances-1024x543.0724f5d9a3fd51b3e9f470535a311dd21d397022.png

  Links:

  • aws.amazon.com/ec2/instance-types/p3
• [AWS] EC2 Inf1 Instances
  Built from the ground up to support machine learning inference applications, featuring up to 16 AWS Inferentia chips.
  Amazon EC2 Inf1 instances are built from the ground up to support machine learning inference applications. Inf1 instances feature up to 16 AWS Inferentia chips, high-performance machine learning inference chips designed and built by AWS. In addition, we’ve coupled the Inferentia chips with the latest custom 2nd generation Intel® Xeon® Scalable processors and up to 100 Gbps networking to enable high throughput inference. This powerful configuration enables Inf1 instances to deliver up to 3x higher throughput and up to 40% lower cost per inference than Amazon EC2 G4 instances, which were already the lowest cost instance for machine learning inference available in the cloud. Using Inf1 instances, customers can run large scale machine learning inference applications like image recognition, speech recognition, natural language processing, personalization, and fraud detection, at the lowest cost in the cloud.

  

  Source: https://d1.awsstatic.com/re19/Diagram_Inf1.15218e96c3d581221974dcd7a8d13c063f75d5a3.png

  Links:

  • aws.amazon.com/ec2/instance-types/inf1
• [AWS] EC2 G4 Instances
  G4 instances provide the latest generation NVIDIA T4 GPUs, AWS custom Intel Cascade Lake CPUs, up to 100 Gbps of networking throughput, and up to 1.8 TB of local NVMe storage.
  Amazon EC2 G4 instances deliver the industry’s most cost-effective and versatile GPU instance for deploying machine learning models in production and graphics-intensive applications. G4 instances provide the latest generation NVIDIA T4 GPUs, AWS custom Intel Cascade Lake CPUs, up to 100 Gbps of networking throughput, and up to 1.8 TB of local NVMe storage. G4 instances are offered in different instance sizes with access to one GPU or multiple GPUs with different amounts of vCPU and memory - giving you the flexibility to pick the right instance size for your applications.
  
  G4 instances are optimized for machine learning application deployments (inference), such as image classification, object detection, recommendation engines, automated speech recognition, and language translation that push the boundary on AI-innovation and latency.
  
  These instances also bring high performance to graphics-intensive applications, such as remote graphics workstations, video transcoding and game streaming in the cloud. With access to NVIDIA Quadro Workstations at no additional cost, G4 instances are the most cost-effective cloud platform for virtual workstations.

  Links:

  • aws.amazon.com/ec2/instance-types/g4
• [AWS] EC2 C5 Instances
  C5 instances offer the lowest price per vCPU in the Amazon EC2 family and are ideal for running advanced compute-intensive workloads.
  Amazon EC2 C5 instances are the next generation of the Amazon EC2 Compute Optimized instance family. C5 instances offer the lowest price per vCPU in the Amazon EC2 family and are ideal for running advanced compute-intensive workloads. This includes workloads such as high performance web servers, high-performance computing (HPC), batch processing, ad serving, highly scalable multiplayer gaming, video encoding, scientific modelling, distributed analytics and machine/deep learning inference.

  

  Source: https://d1.awsstatic.com/products/EC2/C5/ReInvent_HA_C5_EDITORIAL.16c19223eb3a4843538e36747496fae00937c3eb.png

  Links:

  • aws.amazon.com/ec2/instance-types/c5
• [AWS] EC2 F1 Instances
  Use field-programmable gate arrays (FPGAs) to enable delivery of custom hardware accelerations.
  Amazon EC2 F1 instances use FPGAs to enable delivery of custom hardware accelerations. F1 instances are easy to program and come with everything you need to develop, simulate, debug, and compile your hardware acceleration code, including an FPGA Developer AMI and supporting hardware level development on the cloud. Using F1 instances to deploy hardware accelerations can be useful in many applications to solve complex science, engineering, and business problems that require high bandwidth, enhanced networking, and very high compute capabilities. Good target applications for F1 are ones that have a modest number of distinct operations that account for significant portions of application run-time. Examples of such applications include big data analytics, genomics, electronic design automation (EDA), image and video processing, compression, security, and search/analytics. Customers are using F1 instances when in need of processing large amounts of data at lower overall costs, as well as when the acceleration helps significantly reduce compute time. F1 instances provide diverse development environments: from low-level hardware developers to software developers who are more comfortable with C/C++ and openCL environments (available on our GitHub). Once your FPGA design is complete, you can register it as an Amazon FPGA Image (AFI), and deploy it to your F1 instance in just a few clicks. You can reuse your AFIs as many times as you like, and across as many F1 instances as you like. There is no software charge for the development tools when using the FPGA developer AMI and you can program the FPGAs on your F1 instance as many times as you like with no additional fees.

  

  Source: https://d1.awsstatic.com/Digital%20Marketing/sitemerch/ups/SiteMerch-EC2-Instances_accelerated-trial_2up.dc41806beccd08dedfc8d890a612a7921cac9fbb.png

  Links:

  • aws.amazon.com/ec2/instance-types/f1
• [AWS] IoT Greengrass
  Bring local compute, messaging, data management, sync, and ML inference capabilities to edge devices.
  AWS IoT Greengrass seamlessly extends AWS to edge devices so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. With AWS IoT Greengrass, connected devices can run AWS Lambda functions, Docker containers, or both, execute predictions based on machine learning models, keep device data in sync, and communicate with other devices securely – even when not connected to the Internet.
  
  With AWS IoT Greengrass, you can use familiar languages and programming models to create and test your device software in the cloud, and then deploy it to your devices. AWS IoT Greengrass can be programmed to filter device data, manage the life cyle of that data on the device, and only transmit necessary information back to AWS. You can also connect to third-party applications, on-premises software, and AWS services out-of-the-box with AWS IoT Greengrass Connectors. Connectors also jumpstart device onboarding with pre-built protocol adapter integrations and allow you to streamline authentication via integration with AWS Secrets Manager.

  

  Source: https://d1.awsstatic.com/diagrams/product-page-diagram-Greengrass_How-It-Works.dfbc94bee098d673fa9fdc1d69c022daa7670251.png

  

  Source: https://d1.awsstatic.com/r2018/b/product-page-diagram-AWS-Greengate-Launch_Application-Arch@1.5x.db4d6299f3d918d6c1006764b2c65923f9ff1ceb.png

  

  Source: https://d1.awsstatic.com/r2018/b/Greengrass/Product-Page-Diagram-Greenkey.aac67be473d3825a8a3a93531568c212485771c2.png

  Links:

  • aws.amazon.com/greengrass/

Pre-Configured Virtual Machines and Containers (10)

Pre-configured virtual machines and containers for AI and ML modelling, development and deployment.

Pre-configured virtual machines and containers for AI and ML modelling, development and deployment.

• [AWS] Deep Learning Amazon Machine Images (AMIs)
  Pre-configured environments to quickly build deep learning applications.
  The AWS Deep Learning AMIs provide machine learning practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud, at any scale. You can quickly launch Amazon EC2 instances pre-installed with popular deep learning frameworks and interfaces such as TensorFlow, PyTorch, Apache MXNet, Chainer, Gluon, Horovod, and Keras to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques.
  
  Whether you need Amazon EC2 GPU or CPU instances, there is no additional charge for the Deep Learning AMIs – you only pay for the AWS resources needed to store and run your applications.

  

  Source: images/aws_ml_amis.png

  Links:

  • aws.amazon.com/machine-learning/amis
• [AWS] Deep Learning Containers
  Quickly set up deep learning environments with optimized, pre-packaged container images.
  AWS Deep Learning Containers (AWS DL Containers) are Docker images pre-installed with deep learning frameworks to make it easy to deploy custom machine learning (ML) environments quickly by letting you skip the complicated process of building and optimizing your environments from scratch. AWS DL Containers support TensorFlow, PyTorch, and Apache MXNet. You can deploy AWS DL Containers on Amazon Sagemaker, Amazon Elastic Kubernetes Service (Amazon EKS), self-managed Kubernetes on Amazon EC2, Amazon Elastic Container Service (Amazon ECS). The containers are available through Amazon Elastic Container Registry (Amazon ECR) and AWS Marketplace at no cost--you pay only for the resources that you use. Get started with this tutorial.
  
  Docker containers are a popular way to deploy custom ML environments that run consistently in multiple environments. But building and testing container images for deep learning is hard, error-prone, and can take days due to software dependencies and version compatibility issues. These images also need to be optimized to distribute and scale ML workloads efficiently across a cluster of instances, which requires specialized expertise. This process has to be repeated when framework updates are released. All of this is undifferentiated heavy lifting that takes valuable developer time and slows down your pace of innovation.
  
  AWS DL Containers provide Docker images that are pre-installed and tested with the latest versions of popular deep learning frameworks and the libraries they require. AWS DL Containers come optimized to distribute ML workloads efficiently on clusters of instances on AWS, so that you get high performance and scalability right away.

  

  Source: images/aws_dl_containers.png

  Links:

  • aws.amazon.com/machine-learning/containers
• TensorFlow on [AWS]
  Deep learning in the cloud made simple.
  TensorFlow™ enables developers to quickly and easily get started with deep learning in the cloud. The framework has broad support in the industry and has become a popular choice for deep learning research and application development, particularly in areas such as computer vision, natural language understanding and speech translation.
  
  You can get started on AWS with a fully-managed TensorFlow experience with Amazon SageMaker, a platform to build, train, and deploy machine learning models at scale. Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with TensorFlow and other popular frameworks including Apache MXNet, PyTorch, Caffe, Caffe2, Chainer, Gluon, Keras, and Microsoft Cognitive Toolkit.

  Links:

  • aws.amazon.com/tensorflow/
• PyTorch on [AWS]
  Easily build and deploy deep learning models in the cloud.
  PyTorch is an open source, deep learning framework that makes it easy to develop machine learning models and deploy them to production. PyTorch provides a hybrid front-end that allows developers to iterate quickly on their models in the prototyping stage without sacrificing performance in the production stage. Using this hybrid front-end, developers can seamlessly transition between eager mode, which performs computations immediately for easy development, and graph mode which creates computational graphs for efficient execution in production environment. PyTorch supports dynamic computation graphs, which provides a flexible structure that is intuitive to work with and easy to debug. PyTorch also offers distributed training, deep integration into Python, and a rich ecosystem of tools and libraries, making it popular with researchers and engineers. You can get started on AWS with a fully-managed PyTorch experience with Amazon SageMaker, a platform to easily build, train, and deploy machine learning models at scale. If you need to build custom environments and workflows, you can use the PyTorch integration with the AWS Deep Learning AMIs.

  Links:

  • aws.amazon.com/pytorch/
• Apache MXNet on [AWS]
  Build machine learning applications that train quickly and run anywhere.
  Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for machine learning.
  
  MXNet includes the Gluon interface that allows developers of all skill levels to get started with deep learning on the cloud, on edge devices, and on mobile apps. In just a few lines of Gluon code, you can build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation, and personalization.
  
  You can get started with MxNet on AWS with a fully-managed experience using Amazon SageMaker, a platform to build, train, and deploy machine learning models at scale. Or, you can use the AWS Deep Learning AMIs to build custom environments and workflows with MxNet as well as other frameworks including TensorFlow, PyTorch, Chainer, Keras, Caffe, Caffe2, and Microsoft Cognitive Toolkit.

  Links:

  • aws.amazon.com/mxnet
• [Google Cloud] Deep Learning VM Image
  Preconfigured VMs for deep learning applications.
  Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn and more. You can also easily add Cloud GPU and Cloud TPU support.

  Links:

  • cloud.google.com/deep-learning-vm
• [Google Cloud] Deep Learning Containers
  Preconfigured and optimized containers for deep learning environments.
  Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.

  

  Source: https://lh3.googleusercontent.com/-E9IU4jICKASGO4nGfcTtJa93bgWXnFA2o8zai5Ayk7OI93U7AZdQQj3hc0JTVKBhvPmGpo_jebVBw=e14-rj-sc0xffffff-w2540

  Links:

  • cloud.google.com/ai-platform/deep-learning-containers
• [Google Cloud] AI Platform Notebooks
  An enterprise notebook service to launch projects in minutes.
  AI Platform Notebooks is a managed service whose integrated JupyterLab environment makes it easy to create instances that come pre-installed with the latest data science and ML frameworks and integrate with BigQuery, Cloud Dataproc, and Cloud Dataflow for easy development and deployment.

  

  Source: https://lh3.googleusercontent.com/jXKgFfRZMYEO2Dl81SENWsvj0crnYUmR-8H7UFiHwPxI5BfIJjN23xVeUOdL4IZMu0oO38zE59AM=e14-rj-sc0xffffff-w2540

  

  Source: https://cloudx-bricks-prod-bucket.storage.googleapis.com/3c8a480a8bac006081696cd9c25b7c8c4c6192523a907a71642faff8f2f2a532.svg

  Links:

  • cloud.google.com/ai-platform-notebooks
• [Google Cloud] TensorFlow Enterprise
  From the creators of TensorFlow.
  TensorFlow Enterprise delivers enterprise-grade support, performance, and managed services for your AI workloads. Together, these services and products can accelerate your software development and ensure the reliability and performance of your AI applications. TensorFlow Enterprise is the only offering brought to you by the creators of TensorFlow.

  

  Source: https://lh3.googleusercontent.com/4vjw4RmhAwcouq--qFCEF1JH26Ytt4p9wvIOCQ9_N7grQPtdl6z3gK73pTjNmvwqL2-RFUAzkvBbzQ=e14-rj-sc0xffffff-w600

  Links:

  • cloud.google.com/tensorflow-enterprise
• [Azure] Data Science Virtual Machines
  Comprehensive pre-configured virtual machines for data science modelling, development and deployment.
  DSVMs are Azure Virtual Machine images, pre-installed, configured and tested with several popular tools that are commonly used for data analytics, machine learning and AI training.

  

  Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/virtual-machines/data-science-virtual-machines/data-science-diagram.jpg

  

  Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/virtual-machines/data-science-virtual-machines/diagram-2.svg

  Links:

  • azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines

Machine Learning as a Service (MLaaS)

A number of services that offer machine learning tools as a part of cloud computing services.

Machine learning as a service (MLaaS) refers to a number of services that offer machine learning tools as a part of cloud computing services. The main benefit of this solution is that customers can get started with machine learning applications quickly without installing specific software or provisioning their own servers. All the actual computations are handled by the provider’s own data centers.

MLaaS providers offer services for data transformation, predictive analytics, data visualization, and advanced machine learning algorithms. Currently, the major MLaaS platforms suggest ready-made solutions for the majority of popular machine learning applications, including recommender systems, forecasting, image and video analysis, advanced text analytics, machine translation, automated transcription, speech generation, and conversational agents.

Links:

• https://www.topbots.com/comprehensive-guide-to-mlaas/

Platforms (6)

Fully-managed generic cloud machine-learning platforms.

Fully-managed generic cloud machine-learning platforms.

• [AWS] SageMaker
  A fully-managed cloud machine-learning platform.
  Amazon SageMaker is a cloud machine-learning platform that was launched in November 2017. SageMaker enables developers to create, train, and deploy machine-learning (ML) models in the cloud. SageMaker also enables developers to deploy ML models on embedded systems and edge-devices. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.
  
  Traditional ML development is a complex, expensive, iterative process made even harder because there are no integrated tools for the entire machine learning workflow. You need to stitch together tools and workflows, which is time-consuming and error-prone. SageMaker solves this challenge by providing all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost.

  

  Source: https://d1.awsstatic.com/re19/Sagemaker/SageMaker_Overview-Chart.247eaea6e41ddca8299c5a9a9e91b5d78b751c38.png

  Links:

  • aws.amazon.com/sagemaker
  • en.wikipedia.org/wiki/Amazon_SageMaker
• [AWS] SageMaker Neo
  Enables developers to train machine learning models once and run them anywhere in the cloud and at the edge.
  Amazon SageMaker Neo enables developers to train machine learning models once and run them anywhere in the cloud and at the edge. Amazon SageMaker Neo optimizes models to run up to twice as fast, with less than a tenth of the memory footprint, with no loss in accuracy.
  
  Developers spend a lot of time and effort to deliver accurate machine learning models that can make fast, low-latency predictions in real-time. This is particularly important for edge devices where memory and processing power tend to be highly constrained, but latency is very important. For example, sensors in autonomous vehicles typically need to process data in a thousandth of a second to be useful, so a round trip to the cloud and back isn’t possible. Also, there is a wide array of different hardware platforms and processor architectures for edge devices. To achieve high performance, developers need to spend weeks or months hand-tuning their model for each one. Also, the complex tuning process means that models are rarely updated after they are deployed to the edge. Developers miss out on the opportunity to retrain and improve models based on the data the edge devices collect.
  
  Amazon SageMaker Neo automatically optimizes machine learning models to perform at up to twice the speed with no loss in accuracy. You start with a machine learning model built using MXNet, TensorFlow, PyTorch, or XGBoost and trained using Amazon SageMaker. Then you choose your target hardware platform from Intel, NVIDIA, or ARM. With a single click, SageMaker Neo will then compile the trained model into an executable. The compiler uses a neural network to discover and apply all of the specific performance optimizations that will make your model run most efficiently on the target hardware platform. The model can then be deployed to start making predictions in the cloud or at the edge. Local compute and ML inference capabilities can be brought to the edge with AWS IoT Greengrass. To help make edge deployments easy, AWS IoT Greengrass supports Neo-optimized models so that you can deploy your models directly to the edge with over the air updates.
  
  Neo uses Apache TVM and other partner-provided compilers and kernel libraries. Neo is available as open source code as the Neo-AI project under the Apache Software License, enabling developers to customize the software for different devices and applications.

  

  Source: https://d1.awsstatic.com/r2018/r/Neo/Product-Page-Diagram_Neo-How-it-Works.9845ca7e23290bc849f36cb947ef81bd967825ef.png

  Links:

  • aws.amazon.com/sagemaker/neo
• [AWS] Augmented AI (Amazon A2I)
  Easily implement human review of machine learning predictions.
  Amazon Augmented AI (Amazon A2I) makes it easy to build the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers.
  
  Many machine learning applications require humans to review low confidence predictions to ensure the results are correct. For example, extracting information from scanned mortgage application forms can require human review in some cases due to low-quality scans or poor handwriting. But building human review systems can be time consuming and expensive because it involves implementing complex processes or “workflows”, writing custom software to manage review tasks and results, and in many cases, managing large groups of reviewers.
  
  Amazon A2I makes it easy to build and manage human reviews for machine learning applications. Amazon A2I provides built-in human review workflows for common machine learning use cases, such as content moderation and text extraction from documents, which allows predictions from Amazon Rekognition and Amazon Textract to be reviewed easily. You can also create your own workflows for ML models built on Amazon SageMaker or any other tools. Using Amazon A2I, you can allow human reviewers to step in when a model is unable to make a high confidence prediction or to audit its predictions on an on-going basis.

  

  Source: https://d1.awsstatic.com/a2i/Product-Page-Diagram_A2I@2x.2fe2e8e5eed05fa5045835c3f3f04e23a9245e9c.png

  Links:

  • aws.amazon.com/augmented-ai
• [Google Cloud] AI Hub
  Hosted repository of plug-and-play AI components.
  Google Cloud’s AI Hub provides enterprise-grade sharing capabilities, including end-to-end AI pipelines and out-of-the-box algorithms, that let your organization privately host AI content to foster reuse and collaboration among internal developers and users. Enterprise users can find AI components built by other teams within an organization and access AI content published by Google AI, Google Cloud AI, and Google Cloud Partners. You can also easily deploy unique Google Cloud AI and Google AI technologies for experimentation and production on Google Cloud and hybrid infrastructures.

  

  Source: https://cloud.google.com/images/ai-hub/hero-ai-hub-visual_2x.png

  Links:

  • cloud.google.com/ai-hub
• [Azure] Machine Learning Studio
  A collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data.
  Microsoft Azure Machine Learning Studio (classic) is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. Azure Machine Learning Studio (classic) publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel.

  

  Source: https://docs.microsoft.com/en-us/azure/machine-learning/studio/media/what-is-ml-studio/azure-ml-studio-diagram.jpg

  Links:

  • docs.microsoft.com/en-us/azure/machine-learning/studio/what-is-ml-studio
• [Azure] Automated Machine Learning
  Automatically build machine learning models with speed and scale
  Empower professional and non-professional data scientists to build machine learning models rapidly. Automate time-consuming and iterative tasks of model development using breakthrough research—and accelerate time to market.

  

  Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/machine-learning/automatedml/learning-models.gif?637227423860488044

  

  Source: https://docs.microsoft.com/en-us/azure/machine-learning/studio/media/what-is-ml-studio/ml_studio_overview_v1.1.png

  Links:

  • azure.microsoft.com/en-us/services/machine-learning/automatedml

Automated Machine Learning (AutoML) (2)

Automating the process of applying machine learning to real-world problems.

Automated machine learning (AutoML) is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. AutoML was proposed as an artificial intelligence-based solution to the ever-growing challenge of applying machine learning. The high degree of automation in AutoML allows non-experts to make use of machine learning models and techniques without requiring to become an expert in this field first.

Automating the process of applying machine learning end-to-end additionally offers the advantages of producing simpler solutions, faster creation of those solutions, and models that often outperform hand-designed models.

Links:

• https://en.wikipedia.org/wiki/Automated_machine_learning

• [AWS] SageMaker Autopilot
  Automatically create machine learning models with full visibility.
  Amazon SageMaker Autopilot automatically trains and tunes the best machine learning models for classification or regression, based on your data while allowing to maintain full control and visibility.
  
  Building machine learning (ML) models has traditionally required a binary choice. On one hand, you could manually prepare the features, select the algorithm, and optimize the model parameters in order to have full control over the model design and understand all the thought that went into creating it. However, this approach requires deep ML expertise. On the other hand, if you don’t have that expertise, you could use an automated approach (AutoML) to model generation that takes care of all of the heavy lifting, but provides very little visibility into how the model was created. While a model created with AutoML can work well, you may have less trust in it because you can’t understand what went into it, you can’t recreate it, and you can’t learn best practices which may help you in the future.
  
  Amazon SageMaker Autopilot eliminates this choice, allowing you to automatically build machine learning models without compromises. With SageMaker Autopilot, you provide a tabular dataset and select the target column to predict, which can be a number (such as a house price, called regression), or a category (such as spam/not spam, called classification). SageMaker Autopilot will automatically explore different solutions to find the best model. You then can directly deploy the model to production with just one click, or iterate on the recommended solutions with Amazon SageMaker Studio to further improve the model quality.

  

  Source: https://d1.awsstatic.com/re19/Sagemaker/Product-Page-Diagram_SageMaker-Flintstone.6505ea48b82d1438a14901938b35b7871087a488.png

  Links:

  • aws.amazon.com/sagemaker/autopilot
• [Google Cloud] AutoML
  Train high-quality custom machine learning models with minimal effort and machine learning expertise.
  Cloud AutoML is a suite of machine learning products that enables developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology.

  

  Source: https://cloud.google.com/images/products/automl/creating-ml-solutions.svg

  Links:

  • cloud.google.com/automl

Annotators (2)

Tools for human annotation and labeling od datasets.

Annotation in machine learning is the process of labeling data, which could be in the form of text, images, audio, etc. For example, you can label music by genre or language. In machine learning, computers can use the annotated data to learn to recognize similar patterns when presented with new data.

Links:

• https://www.quora.com/What-is-annotation-in-machine-learning
• https://en.wikipedia.org/wiki/Labeled_data

• [AWS] SageMaker Ground Truth
  Build highly accurate training datasets using machine learning and reduce data labeling costs by up to 70%.
  Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly. SageMaker Ground Truth offers easy access to labelers through Amazon Mechanical Turk and provides them with built-in workflows and interfaces for common labeling tasks. You can also use your own labelers or use vendors recommended by Amazon through AWS Marketplace. Additionally, SageMaker Ground Truth can lower your labeling costs by up to 70% using automatic labeling, which works by training Ground Truth from data labeled by humans so that the service learns to label data independently. Successful machine learning models are built on the shoulders of large volumes of high-quality training data. But, the process to create the training data necessary to build these models is often expensive, complicated, and time-consuming. The majority of models created today require a human to manually label data in a way that allows the model to learn how to make correct decisions. For example, building a computer vision system that is reliable enough to identify objects - such as traffic lights, stop signs, and pedestrians - requires thousands of hours of video recordings that consist of hundreds of millions of video frames. Each one of these frames needs all of the important elements like the road, other cars, and signage to be labeled by a human before any work can begin on the model you want to develop.
  
  Amazon SageMaker Ground Truth significantly reduces the time and effort required to create datasets for training to reduce costs. These savings are achieved by using machine learning to automatically label data. The model is able to get progressively better over time by continuously learning from labels created by human labelers.
  
  Where the labeling model has high confidence in its results based on what it has learned so far, it will automatically apply labels to the raw data. Where the labeling model has lower confidence in its results, it will pass the data to humans to do the labeling. The human-generated labels are provided back to the labeling model for it to learn from and improve. Over time, SageMaker Ground Truth can label more and more data automatically and substantially speed up the creation of training datasets.

  

  Source: https://d1.awsstatic.com/r2018/r/Samurai/SamurAI%20Customer%20Assets/Product-Page-Diagram_SamurAI_How-it-works-2.bc19de267c29570783c4add8bb2286ee584fcfbc.png

  Links:

  • aws.amazon.com/sagemaker/groundtruth
• [Google Cloud] Data Labeling
  Managed annotation for high-quality model training data.
  The AI Platform Data Labeling Service enables you to request human labeling for a collection of data that you plan to use to train a custom machine learning model.

  

  Source: https://cloud.google.com/images/ai-platform/hero-banner_2x.png

  

  Source: https://cloud.google.com/images/ai-platform/cloud-ai-platform.svg

  Links:

  • cloud.google.com/ai-platform/data-labeling

Cognitive Services

The objective of cognitive services is to understand and replicate the essence of human intelligence.

The objective of cognitive services is to understand and replicate the essence of human intelligence. Cognitive computing refers to smart systems that learn at scale, reason with purpose, and interact with humans and other smart systems naturally.

Links:

• https://doi.ieeecomputersociety.org/10.1109/MC.2019.2904940

Vision (9)

A category of machine learning that deals with the analysis and interpretation of images and video streams.

Vision is a category of machine learning that deals with the analysis and interpretation of images and video streams. Cameras are increasingly used as input methods, allowing users to convey visually what's hard to describe in text. ML Kit's APIs allow you to offer core experiences like visual search or text extraction as a means to developing camera-first experiences.

Source: https://developers.google.com/ml-kit/images/vision/header-vision_1440.png

Links:

• https://developers.google.com/ml-kit/vision
• https://deepai.org/machine-learning-glossary-and-terms/computer-vision

• [AWS] Rekognition
  Automate your image and video analysis with machine learning.
  Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.
  
  With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required. You simply need to supply images of objects or scenes you want to identify, and the service handles the rest.

  

  Source: https://d1.awsstatic.com/product-marketing/Rekognition/Label-detection.b3858b2903ec631bef4603b6689848ed342a4400.png

  

  Source: https://d1.awsstatic.com/product-marketing/Rekognition/custom-labels.dbc1930e54580d2299cc73184f34fa2bdfffe846.png

  Links:

  • aws.amazon.com/rekognition
• [Google Cloud] Vision AI
  Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more.
  Google Cloud offers two computer vision products that use machine learning to help you understand your images with industry-leading prediction accuracy.
  
  AutoML Vision Automate the training of your own custom machine learning models. Simply upload images and train custom image models with AutoML Vision’s easy-to-use graphical interface; optimize your models for accuracy, latency, and size; and export them to your application in the cloud, or to an array of devices at the edge.
  
  Vision API Google Cloud’s Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.

  

  Source: https://cloud.google.com/images/products/vision/industrial-inspection.svg

  Links:

  • cloud.google.com/vision
• [Google Cloud] Video AI
  Enable powerful content discovery and engaging video experiences.
  Google offers two fully-featured video AI products to make your video library more searchable and valuable.
  
  AutoML Video Intelligence AutoML Video Intelligence has a graphical interface that makes it easy to train your own custom models to classify and track objects within videos, even if you have minimal machine learning experience. It’s ideal for projects that require custom labels which aren’t covered by the pre-trained Video Intelligence API.
  
  Video Intelligence API Video Intelligence API has pre-trained machine learning models that automatically recognize a vast number of objects, places, and actions in stored and streaming video. It’s highly efficient for common use cases and improves over time as new concepts are introduced.

  Links:

  • cloud.google.com/video-intelligence
• [Azure] Computer Vision
  Analyze content in images.
  Boost content discoverability, accelerate text extraction, and create products that more people can use by embedding vision capabilities in your apps. Use visual data processing to label content, from objects to concepts, extract printed and handwritten text, recognize familiar subjects like brands and landmarks, and moderate content. No machine learning expertise is required.

  

  Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/computer-vision/valprop01.svg

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/computer-vision/
• [Azure] Custom Vision
  Customize image recognition to fit your business needs.
  Customize and embed state-of-the-art computer vision for specific domains. Build frictionless customer experiences, optimize manufacturing processes, accelerate digital marketing campaigns—and more. No machine learning expertise is required.

  

  Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/custom-vision-service/value-prop-2-image.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/
• [Azure] Face
  Detect and identify people and emotions in images.
  Embed facial recognition into your apps for a seamless and highly secured user experience. No machine learning expertise is required. Features include: face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/face/
• [Azure] Form Recognizer
  Extract text, key-value pairs, and tables from documents.
  Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.

  

  Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/form-recognizer/demo-sample-thumbnails/form-recognizer-demo-sample1.png

  

  Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/form-recognizer/demo-sample-thumbnails/form-recognizer-demo-sample5.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
• [Azure] Ink Recognizer
  Recognize digital ink and handwriting, and pinpoint common shapes.
  Create application experiences that combine pen and paper interaction with powerful digital capabilities. Recognize digital handwriting, common shapes, and the layout of inked documents for various scenarios like notetaking, form-filling, content search, and document annotation.

  

  Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/ink-recognizer/demo-samples/ink-recognizer-demo-sample1.jpg

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/ink-recognizer/
• [Azure] Video Indexer
  Analyze the visual and audio channels of a video, and index its content.
  Automatically extract metadata—such as spoken words, written text, faces, speakers, celebrities, emotions, topics, brands, and scenes—from video and audio files. Access the data within your application or infrastructure, make it more discoverable, and use it to create new over-the-top (OTT) experiences and monetization opportunities. Try the web experience and API with a free trial.

  

  Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/media-services/video-indexer/evolve-user-engagement.png

  Links:

  • azure.microsoft.com/en-us/services/media-services/video-indexer/

Text-to-Speech (3)

The artificial production of human speech.

Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.

Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.

The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. Many computer operating systems have included speech synthesizers since the early 1990s.

A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech.

Source: https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/TTS_System.svg/1100px-TTS_System.svg.png

Links:

• https://en.wikipedia.org/wiki/Speech_synthesis

• [AWS] Polly
  Turn text into lifelike speech using deep learning.
  Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk, and build entirely new categories of speech-enabled products. Polly's Text-to-Speech (TTS) service uses advanced deep learning technologies to synthesize natural sounding human speech. With dozens of lifelike voices across a broad set of languages, you can build speech-enabled applications that work in many different countries.
  
  In addition to Standard TTS voices, Amazon Polly offers Neural Text-to-Speech (NTTS) voices that deliver advanced improvements in speech quality through a new machine learning approach. Polly’s Neural TTS technology also supports two speaking styles that allow you to better match the delivery style of the speaker to the application: a Newscaster reading style that is tailored to news narration use cases, and a Conversational speaking style that is ideal for two-way communication like telephony applications.
  
  Finally, Amazon Polly Brand Voice can create a custom voice for your organization. This is a custom engagement where you will work with the Amazon Polly team to build an NTTS voice for the exclusive use of your organization. Learn more here.

  

  Source: https://d1.awsstatic.com/Products/product-name/diagrams/product-page-diagram_Amazon-Polly_Content-Creation.6d4a1496e5698966714a3999c8d88387d7c4dd89.png

  

  Source: https://d1.awsstatic.com/Products/product-name/diagrams/product-page-diagram_Amazon-Polly_eLearning.7987d744e6be4bc811a811f44a9e43dd22367a53.png

  

  Source: https://d1.awsstatic.com/Products/product-name/diagrams/product-page-diagram_Amazon-Polly_Telephony.f8b7fa10d860dcc52deecbb8c4815b37a0d57362.png

  Links:

  • aws.amazon.com/polly
• [Google Cloud] Text-to-Speech
  Text-to-speech conversion powered by machine learning.
  Google Cloud Text-to-Speech converts text into human-like speech in more than 180 voices across 30+ languages and variants. It applies groundbreaking research in speech synthesis (WaveNet) and Google's powerful neural networks to deliver high-fidelity audio. With this easy-to-use API, you can create lifelike interactions with your users that transform customer service, device interaction, and other applications.

  Links:

  • cloud.google.com/text-to-speech
• [Azure] Text to Speech
  Convert text to lifelike speech for more natural interfaces.
  Use Text to Speech —part of the Speech service— to build apps and services that speak naturally. Bring your solutions to life with dozens of voices in a wide range of languages. Create lifelike voices with the Neural Text to Speech capability built on breakthrough research in speech synthesis technology. Customize models to create a unique voice for your solution and brand.

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/scenarios/multi-device-conversation.png

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/voice-assistants/overview.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/text-to-speech

Speech-to-Text (4)

The recognition and translation of spoken language into text.

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields.

Some speech recognition systems require "training" (also called "enrollment") where an individual speaker reads text or isolated vocabulary into the system. The system analyzes the person's specific voice and uses it to fine-tune the recognition of that person's speech, resulting in increased accuracy. Systems that do not use training are called "speaker independent" systems. Systems that use training are called "speaker dependent".

Speech recognition applications include voice user interfaces such as voice dialing (e.g. "call home"), call routing (e.g. "I would like to make a collect call"), domotic appliance control, search key words (e.g. find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g. a radiology report), determining speaker characteristics, speech-to-text processing (e.g., word processors or emails), and aircraft (usually termed direct voice input).

The term voice recognition or speaker identification refers to identifying the speaker, rather than what they are saying. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on a specific person's voice or it can be used to authenticate or verify the identity of a speaker as part of a security process.

From the technology perspective, speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data. The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems.

Links:

• https://en.wikipedia.org/wiki/Speech_recognition

• [AWS] Transcribe
  Automatically convert speech to text.
  Amazon Transcribe makes it easy for developers to add speech-to-text capability to their applications. Audio data is virtually impossible for computers to search and analyze. Therefore, recorded speech needs to be converted to text before it can be used in applications. Historically, customers had to work with transcription providers that required them to sign expensive contracts and were hard to integrate into their technology stacks to accomplish this task. Many of these providers use outdated technology that does not adapt well to different scenarios, like low-fidelity phone audio common in contact centers, which results in poor accuracy.
  
  Amazon Transcribe uses a deep learning process called automatic speech recognition (ASR) to convert speech to text quickly and accurately. Amazon Transcribe can be used to transcribe customer service calls, to automate closed captioning and subtitling, and to generate metadata for media assets to create a fully searchable archive. You can use Amazon Transcribe Medical to add medical speech to text capabilities to clinical documentation applications.

  Links:

  • aws.amazon.com/transcribe
• [Google Cloud] Speech-to-Text
  Speech-to-text conversion powered by machine learning.
  Google Speech-to-Text enables developers to convert audio to text by applying powerful neural network models in an easy-to-use API. The API recognizes more than 120 languages and variants to support your global user base. You can enable voice command-and-control, transcribe audio from call centers, and more. It can process real-time streaming or prerecorded audio, using Google’s machine learning technology.

  Links:

  • cloud.google.com/speech-to-text
• [Azure] Speech to Text
  Convert spoken audio to text for more natural interactions
  Use Speech to Text—part of the Speech service—to swiftly convert audio into text from a variety of sources. Customize models to overcome common speech recognition barriers, such as unique vocabularies, speaking styles, or background noise. Make audio more accessible by helping everyone follow and engage in conversations in real-time.

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/scenarios/conversation-transcription-service.png

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/voice-assistants/task-completion-examples.png

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/voice-assistants/custom-commands-flow.png

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/voice-assistants/overview-directlinespeech.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/speech-to-text
• [Azure] Speaker Recognition
  Identify individual speakers or use speech as a means of verification with Speaker Recognition
  Identify who is speaking. The API can be used to determine the identity of an unknown speaker. Input audio of the unknown speaker is paired against a group of selected speakers, and in the case there is a match found, the speaker’s identity is returned.
  
  We have selected 5 different US presidents and enrolled them to the service using one of the speeches they gave. To see how the demo works, select a speech for one of the presidents by clicking on the sample audios below, or upload one of your own, to test how to automatically identify which president is speaking.

  

  Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/shared/cognitive-services-demos/speaker-verification/president-6.jpg

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/speaker-recognition

Image-to-Text (1)

Conversion of images of typed, handwritten or printed text into machine-encoded text.

Optical character recognition or optical character reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example from a television broadcast).

Widely used as a form of data entry from printed paper data records – whether passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, or any suitable documentation – it is a common method of digitizing printed texts so that they can be electronically edited, searched, stored more compactly, displayed on-line, and used in machine processes such as cognitive computing, machine translation, (extracted) text-to-speech, key data and text mining. OCR is a field of research in pattern recognition, artificial intelligence and computer vision.

Early versions needed to be trained with images of each character, and worked on one font at a time. Advanced systems capable of producing a high degree of recognition accuracy for most fonts are now common, and with support for a variety of digital image file format inputs. Some systems are capable of reproducing formatted output that closely approximates the original page including images, columns, and other non-textual components.

Source: https://upload.wikimedia.org/wikipedia/commons/thumb/8/81/Portable_scanner_and_OCR_%28video%29.webm/300px--Portable_scanner_and_OCR_%28video%29.webm.jpg

Links:

• https://en.wikipedia.org/wiki/Optical_character_recognition

• [AWS] Textract
  Easily extract text and data from virtually any document.
  Amazon Textract is a service that automatically extracts text and data from scanned documents. Amazon Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables.
  
  Many companies today extract data from documents and forms through manual data entry that’s slow and expensive or through simple optical character recognition (OCR) software that requires manual customization or configuration. Rules and workflows for each document and form often need to be hard-coded and updated with each change to the form or when dealing with multiple forms. If the form deviates from the rules, the output is often scrambled and unusable.
  
  Amazon Textract overcomes these challenges by using machine learning to instantly “read” virtually any type of document to accurately extract text and data without the need for any manual effort or custom code. With Textract you can quickly automate document workflows, enabling you to process millions of document pages in hours. Once the information is captured, you can take action on it within your business applications to initiate next steps for a loan application or medical claims processing. Additionally, you can create smart search indexes, build automated approval workflows, and better maintain compliance with document archival rules by flagging data that may require redaction.

  

  Source: https://d1.awsstatic.com/r2018/r/Hieroglyph/feature-diagram_hieroglyph_OCR.897386f6c5d9c472c423aa8fab9064ed19dc1984.png

  

  Source: https://d1.awsstatic.com/r2018/r/Hieroglyph/feature-diagram_hieroglyph_key-value-pair-extraction.673eed38b7860d3c0f295c23e2a10e2fff6a5920.png

  

  Source: https://d1.awsstatic.com/r2018/r/Hieroglyph/feature-diagram_hieroglyph_table-extraction.2db019dc23526ca379d6878a779dfcb057193f1a.png

  Links:

  • aws.amazon.com/textract

Computational Linguistics (10)

The use of algorithms to determine properties of natural, human language so that computers can understand what humans have written or said.

The use of algorithms to determine properties of natural, human language so that computers can understand what humans have written or said. NLP includes teaching computer systems how to extract data from bodies of written text, translate from one language to another, and recognize printed or handwritten words. Notably, NLP is the field that allows for our everyday use of virtual assistants such as Siri.

Links:

• https://deepai.org/machine-learning-glossary-and-terms/natural-language-processing
• https://en.wikipedia.org/wiki/Computational_linguistics

• [AWS] Translate
  Natural and accurate language translation.
  Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation is a form of language translation automation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule-based translation algorithms. Amazon Translate allows you to localize content - such as websites and applications - for international users, and to easily translate large volumes of text efficiently.

  Links:

  • aws.amazon.com/translate
• [AWS] Comprehend
  Discover insights and relationships in text.
  Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. No machine learning experience required.
  
  There is a treasure trove of potential sitting in your unstructured data. Customer emails, support tickets, product reviews, social media, even advertising copy represents insights into customer sentiment that can be put to work for your business. The question is how to get at it? As it turns out, Machine learning is particularly good at accurately identifying specific items of interest inside vast swathes of text (such as finding company names in analyst reports), and can learn the sentiment hidden inside language (identifying negative reviews, or positive customer interactions with customer service agents), at almost limitless scale.
  
  Amazon Comprehend uses machine learning to help you uncover the insights and relationships in your unstructured data. The service identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech; and automatically organizes a collection of text files by topic. You can also use AutoML capabilities in Amazon Comprehend to build a custom set of entities or text classification models that are tailored uniquely to your organization’s needs.

  

  Source: https://d1.awsstatic.com/r2018/r/hera/product-page-diagram-AWS-Hera-Launch_How-It-Works@1.5x.78220a4feceef787705be35c1bb772b1b2038b97.png

  

  Source: https://d1.awsstatic.com/Comprehend/updated-diagrams/product-page-diagram_Amazon-Comprehend_Voice-Of-Customer.93d8f858f529bec19d1899c9d8a5625d3989e621.png

  

  Source: https://d1.awsstatic.com/Comprehend/updated-diagrams/product-page-diagram_Amazon-Comprehend_Semantic-Search.242a46f358903fc05285babc5c35de588313c52d.png

  

  Source: https://d1.awsstatic.com/Comprehend/updated-diagrams/product-page-diagram_Amazon-Comprehend_Knowledge-Management-Discovery.24a511406c408bb3e6c921b3446780c76dd461af.png

  

  Source: https://d1.awsstatic.com/Comprehend/updated-diagrams/product-page-diagram_Amazon-Comprehend_Customer-Support-Ticket-Handling.77c4681d938bfc004574b95ef9dfa41691b2c3c8.png

  

  Source: https://d1.awsstatic.com/r2018/r/hera/product-page-diagram-AWS-Hera-Launch_Clinical-Trial-Recruitment@1.5x.35a35b017bde63978ba29c2364d2507c99920450.png

  Links:

  • aws.amazon.com/comprehend
• [Google Cloud] Translate
  Dynamically translate between languages using Google machine learning.
  Developers, translators, and localization experts with limited machine learning expertise can quickly create high-quality, production-ready models. Just upload translated language pairs and AutoML Translation will train a custom model that you can scale and adapt to meet your domain specific needs.

  

  Source: https://cloud.google.com/images/products/artwork/how-automl-translated-language-works_2x.png

  Links:

  • cloud.google.com/translate
• [Google Cloud] Natural Language
  Derive insights from unstructured text using Google machine learning.
  Natural Language uses machine learning to reveal the structure and meaning of text. You can extract information about people, places, and events, and better understand social media sentiment and customer conversations. Natural Language enables you to analyze text and also integrate it with your document storage on Cloud Storage.

  

  Source: https://cloud.google.com/images/natural-language/automl-works.svg

  Links:

  • cloud.google.com/natural-language
• [Azure] Speech Translation
  Easily integrate real-time speech translation to your app.
  The Speech Translator API is a cloud-based automatic translation service which enables developers to add end-to-end, real-time, speech translations to their applications or services.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/speech-translation
• [Azure] Translator Text
  Easily conduct real-time text translation with a simple REST API call.
  Microsoft Translator API is a neural machine translation service that developers can easily integrate into their applications websites, tools, or any solution requiring multi-language support such as website localization, e-commerce, customer support, messaging applications, internal communication, and more.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/translator-text-api
• [Azure] Immersive Reader
  Help readers of all abilities comprehend text using audio and visual cues.
  Immersive Reader is an Azure Cognitive Service that lets you embed text reading and comprehension capabilities into applications. Help users of any age and reading ability with features like reading aloud, translating languages, and focusing attention through highlighting and other design elements. Azure is the only major cloud provider offering this type of reading technology. No machine learning expertise is required.

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/immersive-reader/media/picture-dictionary.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/immersive-reader
• [Azure] Language Understanding
  A machine learning-based service to build natural language understanding into apps, bots, and IoT devices.
  Designed to identify valuable information in conversations, Language Understanding interprets user goals (intents) and distills valuable information from sentences (entities), for a high quality, nuanced language model. Language Understanding integrates seamlessly with the Speech service for instant Speech to Intent processing, and with the Azure Bot Service, making it easy to create a sophisticated bot.

  

  Source: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/media/information-chatbot.png

  

  Source: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/media/commerce-chatbot.png

  

  Source: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/media/controlling-iot-devices-using-voice.svg

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/language-understanding-intelligent-service
• [Azure] QnA Maker
  Go from semi-structured content to bot in minutes.
  QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents. Answer users’ questions with the best answers from the QnAs in your knowledge base—automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior.

  

  Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/cognitive-services/qna-maker/chit-chat.png

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/media/qnamaker-overview-learnabout/bot-chat-with-qnamaker.png

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/media/qnamaker-overview-learnabout/example-question-and-answer-with-metadata.png

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/media/qnamaker-overview-learnabout/development-cycle.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/qna-maker
• [Azure] Text Analytics
  Detect sentiment, key phrases, named entities and language from your text.
  The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, language detection, and named entity recognition.
  
  The API is a part of Azure Cognitive Services, a collection of machine learning and AI algorithms in the cloud for your development projects.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/text-analytics

AI-Powered Application Services

Specialized services build using AI techniques.

Specialized services build using AI techniques.

Links:

• https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence

AI Apps and Agents (Bots) (3)

A dialogue system that conducts natural language processing and responds automatically using human language.

A dialogue system, or conversational agent (CA), is a computer system intended to converse with a human. Dialogue systems employed one or more of text, speech, graphics, haptics, gestures, and other modes for communication on both the input and output channel.

The elements of a dialogue system are not defined, however they are different from chatbot. The typical GUI wizard engages in a sort of dialog, but it includes very few of the common dialogue system components, and dialog state is trivial.

Links:

• https://en.wikipedia.org/wiki/Dialogue_system
• https://alexeyza.com/pdf/ieee2018swt.pdf
• https://deepai.org/machine-learning-glossary-and-terms/conversational-agent

• [AWS] Lex
  Conversational interfaces for your applications powered by the same deep learning technologies as Alexa.
  Amazon Lex is a service for building conversational interfaces into any application using voice and text. Amazon Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions. With Amazon Lex, the same deep learning technologies that power Amazon Alexa are now available to any developer, enabling you to quickly and easily build sophisticated, natural language, conversational bots (“chatbots”).
  
  Speech recognition and natural language understanding are some of the most challenging problems to solve in computer science, requiring sophisticated deep learning algorithms to be trained on massive amounts of data and infrastructure. Amazon Lex democratizes these deep learning technologies by putting the power of Amazon Alexa within reach of all developers. Harnessing these technologies, Amazon Lex enables you to define entirely new categories of products made possible through conversational interfaces.
  
  As a fully managed service, Amazon Lex scales automatically, so you don’t need to worry about managing infrastructure. With Amazon Lex, you pay only for what you use. There are no upfront commitments or minimum fees.

  

  Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_Chatbot.b5adc0437cda103fea9ace20fb5f59f4efd57f32.png

  

  Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_InfoBot.57e3395f003ad7be15463ad7cc10d425b9f65e0c.png

  

  Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_ApplicationBot.625139142db01f995e6506698ce04d22610604a8.png

  

  Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_Enterprise.e8867e659d8821dbeffb048b687b479a6d34036d.png

  

  Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_IoT.8e8e02b196e5af5d4b68d891ef3d492839cbb616.png

  Links:

  • aws.amazon.com/lex
• [Azure] Content Moderator
  Machine-assisted content moderation APIs and human review tool for images, text, and videos.
  Detection of potential offensive and unwanted images. Filtering of possible profanity and undesirable text. Moderation of adult and racy content in videos. Built-in review tool for best results.

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/images/content-moderator-mod-api.png

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/images/content-moderator-rev-api.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/content-moderator
• [Google Cloud] Dialogflow
  Create conversational experiences across devices and platforms. Try it free
  Dialogflow is an end-to-end, build-once deploy-everywhere development suite for creating conversational interfaces for websites, mobile applications, popular messaging platforms, and IoT devices. You can use it to build interfaces (such as chatbots and conversational IVR) that enable natural and rich interactions between your users and your business. Dialogflow Enterprise Edition users have access to Google Cloud Support and a service level agreement (SLA) for production deployments.

  

  Source: https://cloud.google.com/dialogflow/docs/images/console-intent-selected.png

  Links:

  • cloud.google.com/dialogflow

AI-Powered Personalization Services (3)

Tailoring a service or a product to accommodate specific individuals, sometimes tied to groups or segments of individuals.

Personalization (broadly known as customization) consists of tailoring a service or a product to accommodate specific individuals, sometimes tied to groups or segments of individuals. A wide variety of organizations use personalization to improve customer satisfaction, digital sales conversion, marketing results, branding, and improved website metrics as well as for advertising. Personalization is a key element in social media and recommender systems.

Links:

• https://en.wikipedia.org/wiki/Personalization

• [AWS] Personalize
  Real-time personalization and recommendation, based on the same technology used at Amazon.com
  Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications.
  
  Machine learning is being increasingly used to improve customer engagement by powering personalized product and content recommendations, tailored search results, and targeted marketing promotions. However, developing the machine-learning capabilities necessary to produce these sophisticated recommendation systems has been beyond the reach of most organizations today due to the complexity. Amazon Personalize allows developers with no prior machine learning experience to easily build sophisticated personalization capabilities into their applications, using machine learning technology perfected from years of use on Amazon.com.
  
  With Amazon Personalize, you provide an activity stream from your application – clicks, page views, signups, purchases, and so forth – as well as an inventory of the items you want to recommend, such as articles, products, videos, or music. You can also choose to provide Amazon Personalize with additional demographic information from your users such as age, or geographic location. Amazon Personalize will process and examine the data, identify what is meaningful, select the right algorithms, and train and optimize a personalization model that is customized for your data. All data analyzed by Amazon Personalize is kept private and secure, and only used for your customized recommendations. You can start serving personalized recommendations via a simple API call. You pay only for what you use, and there are no minimum fees and no upfront commitments.
  
  Amazon Personalize is like having your own Amazon.com machine learning personalization team at your disposal, 24 hours a day.

  

  Source: https://d1.awsstatic.com/r2018/r/Concierge/product-page-diagram_amazon_personalize_how-it-works.3ceac8883c7d6bd67d7cf26d8a7d505520d02a40.png

  Links:

  • aws.amazon.com/personalize
• [Google Cloud] Recommendations AI
  Deliver highly personalized product recommendations at scale.
  Earn your customers’ trust and loyalty by proving how well you understand them. Google has spent years delivering recommended content across flagship properties such as Google Ads, Google Search, and YouTube.
  
  Recommendations AI draws on that experience to deliver personalized recommendations that suit each customer’s tastes and preferences across all your touchpoints. Ask your Google Account Manager about this limited beta opportunity.

  

  Source: https://cloud.google.com/images/recommendations/workflow-diagram.svg?dcb_=0.8029302210032698

  Links:

  • cloud.google.com/recommendations
• [Azure] Personalizer
  An award-winning AI service that delivers a personalized, relevant experience for every user.
  Boost usability and user satisfaction with reinforcement learning–based capabilities only available through Azure. Prioritize relevant content, layouts, and conversations through an easy-to-use API. Use Personalizer as a standalone personalization solution or to complement an existing solution—with no machine learning expertise required.

  

  Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/cognitive-services/personalizer/market-quickly.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/personalizer

AI-Powered Anomaly and Fraud Detector Services (2)

The identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.

Anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

Links:

• https://en.wikipedia.org/wiki/Anomaly_detection
• https://en.wikipedia.org/wiki/Data_analysis_techniques_for_fraud_detection

• [AWS] Fraud Detector
  A fully managed service that makes it easy to identify potentially fraudulent online activities such as online payment fraud and the creation of fake accounts.
  Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities such as online payment fraud and the creation of fake accounts.
  
  Globally each year, tens of billions of dollars are lost to online fraud. Companies conducting business online are especially prone to attacks from bad actors who often exploit different tactics such as creating fake accounts and making payments with stolen credit cards. Companies typically use fraud detection applications to identify fraudsters and stop them before they cause costly business disruptions. However, these applications often rely on business rules that don’t keep up with the changing behaviors of fraudsters. More recent fraud detection applications have tried to use machine learning. But, they frequently use a one-size-fits-all approach based on general data sets and fraud behaviors that aren't specific to your business, which limits their accuracy.
  
  Fraud Detector overcomes these challenges by using your data, machine learning (ML), and more than 20 years of fraud detection expertise from Amazon to automatically identify potentially fraudulent online activity so you can catch more fraud faster. You can create a fraud detection model with just a few clicks and no prior ML experience because Fraud Detector handles all of the ML heavy lifting for you.

  

  Source: https://d1.awsstatic.com/re19/Hawksnest/trigger_rules.ed9d347d3c009622353611805f9557b6a3030e0f.png

  Links:

  • aws.amazon.com/fraud-detector
• [Azure] Anomaly Detector
  Identify potential problems early on.
  Easily embed anomaly detection capabilities into your apps so users can quickly identify problems. Through an API, Anomaly Detector Preview ingests time-series data of all types and selects the best-fitting detection model for your data to ensure high accuracy. Customize the service to detect any level of anomaly and deploy it where you need it most -- from the cloud to the intelligent edge with containers. Azure is the only major cloud provider that offers anomaly detection as an AI service.

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/media/anomaly_detection2.png

  

  Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/cognitive-services/anomaly-detector/diagram-speed-time-insights.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/anomaly-detector

AI-Powered Forecast Services (1)

Making predictions of the future based on past and present data and most commonly by analysis of trends.

Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period.

Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attaching to forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible. In some cases the data used to predict the variable of interest is itself forecast.

Links:

• https://en.wikipedia.org/wiki/Forecasting

• [AWS] Forecast
  Accurate time-series forecasting service, based on the same technology used at Amazon.com, no machine learning experience required.
  Amazon Forecast is a fully managed service that uses machine learning to deliver highly accurate forecasts. Companies today use everything from simple spreadsheets to complex financial planning software to attempt to accurately forecast future business outcomes such as product demand, resource needs, or financial performance. These tools build forecasts by looking at a historical series of data, which is called time series data. For example, such tools may try to predict the future sales of a raincoat by looking only at its previous sales data with the underlying assumption that the future is determined by the past. This approach can struggle to produce accurate forecasts for large sets of data that have irregular trends. Also, it fails to easily combine data series that change over time (such as price, discounts, web traffic, and number of employees) with relevant independent variables like product features and store locations. Based on the same technology used at Amazon.com, Amazon Forecast uses machine learning to combine time series data with additional variables to build forecasts. Amazon Forecast requires no machine learning experience to get started. You only need to provide historical data, plus any additional data that you believe may impact your forecasts. For example, the demand for a particular color of a shirt may change with the seasons and store location. This complex relationship is hard to determine on its own, but machine learning is ideally suited to recognize it. Once you provide your data, Amazon Forecast will automatically examine it, identify what is meaningful, and produce a forecasting model capable of making predictions that are up to 50% more accurate than looking at time series data alone. Amazon Forecast is a fully managed service, so there are no servers to provision, and no machine learning models to build, train, or deploy. You pay only for what you use, and there are no minimum fees and no upfront commitments.

  

  Source: https://d1.awsstatic.com/r2018/r/seer/diagrams/Seer_HowitWorks_Final.44b02658b17d05e9242b450b220f6e0ca4065638.png

  Links:

  • aws.amazon.com/forecast

AI-Powered Software Intelligence (1)

Providing insights into software structure produced by software designed to analyze database structure, software framework and source code to better understand and control complex software systems.

Software Intelligence is insight into software structure produced by software designed to analyze database structure, software framework and source code to better understand and control complex software systems in Information Technology environments. Similarly to Business Intelligence (BI), Software Intelligence is produced by a set of software tools and techniques for the mining of data and software inner-structure. End results are information used by business and software stakeholders to make informed decisions, communicate about software health, measure the efficiency of software development organizations, and prevent software catastrophes.

Links:

• https://en.wikipedia.org/wiki/Software_intelligence
• https://en.wikipedia.org/wiki/Automated_code_review

• [AWS] CodeGuru
  Automate code reviews and identify your most expensive lines of code.
  Amazon CodeGuru is a machine learning service for automated code reviews and application performance recommendations. It helps you find the most expensive lines of code that hurt application performance and keep you up all night troubleshooting, then gives you specific recommendations to fix or improve your code. CodeGuru is powered by machine learning, best practices, and hard-learned lessons across millions of code reviews and thousands of applications profiled on open source projects and internally at Amazon. With CodeGuru, you can find and fix code issues such as resource leaks, potential concurrency race conditions, and wasted CPU cycles. And with low, on-demand pricing, it is inexpensive enough to use for every code review and application you run. CodeGuru supports Java applications today, with support for more languages coming soon. CodeGuru helps you catch problems faster and earlier, so you can build and run better software.

  

  Source: https://d1.awsstatic.com/re19/Screenshot_FIND-YOUR-MOST_Annotated.f36905ca4327545f5f01f475f5610336ef889185.png

  

  Source: https://d1.awsstatic.com/re19/Screenshot_Catch-Code-Issue_2%20-%20Annotations%20LP.df0deb64bbfb02219d429db4d5bd3efd089e2a89.png

  Links:

  • aws.amazon.com/codeguru

AI-Powered Search Services (10)

Leverage automated learning techniques to deliver search capabilities.

The goal of AI-powered search is to leverage automated learning techniques to deliver search capabilities. While many organizations start with basic text search and spend many years trying to manually optimize synonyms lists, business rules, ontologies, field weights, and countless other aspects of their search configuration, some are beginning to realize that most of this process can actually be automated.

Links:

• https://towardsdatascience.com/ai-search-algorithms-every-data-scientist-should-know-ed0968a43a7a
• https://www.manning.com/books/ai-powered-search

• [AWS] Kendra
  Add natural language search capabilities to your apps so your end users can more easily find the information they need.
  Amazon Kendra is a highly accurate and easy to use enterprise search service that’s powered by machine learning. Kendra delivers powerful natural language search capabilities to your websites and applications so your end users can more easily find the information they need within the vast amount of content spread across your company.

  

  Source: https://d1.awsstatic.com/re19/Kendra/WEB_Kendra_Before-After.f62b81392674c05785f547c731a4a087faabf412.png

  Links:

  • aws.amazon.com/kendra
• [Azure] Bing Autosuggest
  Help users complete queries faster by adding intelligent type-ahead capabilities
  With Bing Autosuggest API v7, help users complete queries faster by adding intelligent type-ahead capabilities to your app or website. Empower users to type less and do more with automated and complete search suggestions. With the API v7, discover improved error handling capabilities to troubleshoot and diagnose problems quickly.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/autosuggest
• [Azure] Bing Custom Search
  Create a custom search engine with ad-free results.
  The Bing Custom Search API gives you powerful ranking, a global-scale search index, and document processing with fast, simple setup. The core technology works in four steps—by identifying on-topic sites and images, providing automatic query suggestions, applying the Bing ranker, and delivering relevant search results. Adjust the parameters and access usage insights anytime.

  

  Source: https://docs.microsoft.com/en-us/azure/cognitive-services/Bing-Custom-Search/media/bcs-overview.png

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/bing-custom-search
• [Azure] Bing Entity Search
  Recognize and classify named entities, and find search results based on them.
  Harness the power of the web with the Bing Entity Search API. You can enrich your app by infusing knowledge search into existing content. Bing Entity Search API will identify the most relevant entity based on your searched term, spanning multiple entity types such as famous people, places, movies, TV shows, video games, books, and even local businesses near you.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/bing-entity-search-api
• [Azure] Bing Image Search
  Add a variety of search image options to your app.
  With Bing Image Search API v7, help users scour the web for images. Results include thumbnails, full image URLs, publishing website info, image metadata, and more. With the API v7, discover new sorting and filtering options that simplify finding specific results in image searches and enable paging of results. Try out the demo. Submit a query via the search box or click on one of the provided examples.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/bing-image-search-api
• [Azure] Bing News Search
  Turn any app into a news search resource.
  Search the web for news articles with Bing News Search API v7. Results include details like authoritative image of the news article, related news and categories, provider info, article URL, and date added. With the API v7, discover new sorting and filtering options that simplify finding specific results in trending news topics. Try out the demo. Submit a query via the search box or click on one of the provided examples.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/bing-news-search-api
• [Azure] Bing Spell Check
  Help users identify and fix word breaks, slang, names, homonyms, and brands.
  Help users correct spelling errors, recognize the difference among names, brand names, and slang, as well as understand homophones as they’re typing.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/spell-check
• [Azure] Bing Video Search
  Add advanced video search features to your app.
  With Bing Video Search API v7, find videos across the web. Results provide useful metadata including creator, encoding format, video length, view count, improved & simplified paging, and more. Try out the demo. Submit a query via the search box or click on one of the provided examples.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/bing-video-search-api
• [Azure] Bing Visual Search
  Enable users to search using images.
  Create compelling visual experiences through interpretation of images on a device of your choice. Enrich the search experience with visually similar images and products from your business, and use Bing Visual Search to recognize celebrities, monuments, artwork, and other related objects. Identify barcodes or extract textual information from images to provide rich insights—all through the single API.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/bing-visual-search
• [Azure] Bing Web Search
  Enable safe, ad-free, location-aware search for your users, surfacing relevant information from web results, images, news, videos, and visuals.
  Retrieve web documents indexed by Bing Web Search API v7 and narrow down the results by result type, freshness and more. With API v7, discover improved query performance for your results. Try out the demo. Submit a query via the search box or click on one of the provided examples.

  Links:

  • azure.microsoft.com/en-us/services/cognitive-services/bing-web-search-api

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AI Hardware Accelerators

[AWS] Inferentia

Source: https://d1.awsstatic.com/re19/Inferentia-image.ff8c860375e648fb9f60b27f3125fc21adda3a59.JPG

AI Hardware Accelerators

[Google Cloud] TPU

Source: https://cloud.google.com/images/products/tpu/google-cloud-ai_2x.png

AI Hardware Accelerators

[Google Cloud] TPU

Source: https://cloud.google.com/images/products/tpu/machine-learning-performance_2x.png

AI Hardware Accelerators

[Google Cloud] TPU

Source: https://upload.wikimedia.org/wikipedia/commons/thumb/b/be/Tensor_Processing_Unit_3.0.jpg/440px-Tensor_Processing_Unit_3.0.jpg

AI Hardware Accelerators

[Azure] Graphcore

Source: https://www.graphcore.ai/hubfs/P1-chassis01.jpg

Optimized Hardware Configurations

[AWS] EC2 P3 Instances

Source: https://d1.awsstatic.com/4667-P3Instances-1024x543.0724f5d9a3fd51b3e9f470535a311dd21d397022.png

Optimized Hardware Configurations

[AWS] EC2 Inf1 Instances

Source: https://d1.awsstatic.com/re19/Diagram_Inf1.15218e96c3d581221974dcd7a8d13c063f75d5a3.png

Optimized Hardware Configurations

[AWS] EC2 C5 Instances

Source: https://d1.awsstatic.com/products/EC2/C5/ReInvent_HA_C5_EDITORIAL.16c19223eb3a4843538e36747496fae00937c3eb.png

Optimized Hardware Configurations

[AWS] EC2 F1 Instances

Source: https://d1.awsstatic.com/Digital%20Marketing/sitemerch/ups/SiteMerch-EC2-Instances_accelerated-trial_2up.dc41806beccd08dedfc8d890a612a7921cac9fbb.png

Optimized Hardware Configurations

[AWS] IoT Greengrass

Source: https://d1.awsstatic.com/diagrams/product-page-diagram-Greengrass_How-It-Works.dfbc94bee098d673fa9fdc1d69c022daa7670251.png

Optimized Hardware Configurations

[AWS] IoT Greengrass

Source: https://d1.awsstatic.com/r2018/b/product-page-diagram-AWS-Greengate-Launch_Application-Arch@1.5x.db4d6299f3d918d6c1006764b2c65923f9ff1ceb.png

Optimized Hardware Configurations

[AWS] IoT Greengrass

Source: https://d1.awsstatic.com/r2018/b/Greengrass/Product-Page-Diagram-Greenkey.aac67be473d3825a8a3a93531568c212485771c2.png

Pre-Configured Virtual Machines and Containers

[AWS] Deep Learning Amazon Machine Images (AMIs)

Source: images/aws_ml_amis.png

Pre-Configured Virtual Machines and Containers

[AWS] Deep Learning Containers

Source: images/aws_dl_containers.png

Pre-Configured Virtual Machines and Containers

[Google Cloud] Deep Learning Containers

Source: https://lh3.googleusercontent.com/-E9IU4jICKASGO4nGfcTtJa93bgWXnFA2o8zai5Ayk7OI93U7AZdQQj3hc0JTVKBhvPmGpo_jebVBw=e14-rj-sc0xffffff-w2540

Pre-Configured Virtual Machines and Containers

[Google Cloud] AI Platform Notebooks

Source: https://lh3.googleusercontent.com/jXKgFfRZMYEO2Dl81SENWsvj0crnYUmR-8H7UFiHwPxI5BfIJjN23xVeUOdL4IZMu0oO38zE59AM=e14-rj-sc0xffffff-w2540

Pre-Configured Virtual Machines and Containers

[Google Cloud] AI Platform Notebooks

Source: https://cloudx-bricks-prod-bucket.storage.googleapis.com/3c8a480a8bac006081696cd9c25b7c8c4c6192523a907a71642faff8f2f2a532.svg

Pre-Configured Virtual Machines and Containers

[Google Cloud] TensorFlow Enterprise

Source: https://lh3.googleusercontent.com/4vjw4RmhAwcouq--qFCEF1JH26Ytt4p9wvIOCQ9_N7grQPtdl6z3gK73pTjNmvwqL2-RFUAzkvBbzQ=e14-rj-sc0xffffff-w600

Pre-Configured Virtual Machines and Containers

[Azure] Data Science Virtual Machines

Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/virtual-machines/data-science-virtual-machines/data-science-diagram.jpg

Pre-Configured Virtual Machines and Containers

[Azure] Data Science Virtual Machines

Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/virtual-machines/data-science-virtual-machines/diagram-2.svg

Platforms

[AWS] SageMaker

Source: https://d1.awsstatic.com/re19/Sagemaker/SageMaker_Overview-Chart.247eaea6e41ddca8299c5a9a9e91b5d78b751c38.png

Platforms

[AWS] SageMaker Neo

Source: https://d1.awsstatic.com/r2018/r/Neo/Product-Page-Diagram_Neo-How-it-Works.9845ca7e23290bc849f36cb947ef81bd967825ef.png

Platforms

[AWS] Augmented AI (Amazon A2I)

Source: https://d1.awsstatic.com/a2i/Product-Page-Diagram_A2I@2x.2fe2e8e5eed05fa5045835c3f3f04e23a9245e9c.png

Platforms

[Google Cloud] AI Hub

Source: https://cloud.google.com/images/ai-hub/hero-ai-hub-visual_2x.png

Platforms

[Azure] Machine Learning Studio

Source: https://docs.microsoft.com/en-us/azure/machine-learning/studio/media/what-is-ml-studio/azure-ml-studio-diagram.jpg

Platforms

[Azure] Automated Machine Learning

Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/machine-learning/automatedml/learning-models.gif?637227423860488044

Platforms

[Azure] Automated Machine Learning

Source: https://docs.microsoft.com/en-us/azure/machine-learning/studio/media/what-is-ml-studio/ml_studio_overview_v1.1.png

Automated Machine Learning (AutoML)

[AWS] SageMaker Autopilot

Source: https://d1.awsstatic.com/re19/Sagemaker/Product-Page-Diagram_SageMaker-Flintstone.6505ea48b82d1438a14901938b35b7871087a488.png

Automated Machine Learning (AutoML)

[Google Cloud] AutoML

Source: https://cloud.google.com/images/products/automl/creating-ml-solutions.svg

Annotators

[AWS] SageMaker Ground Truth

Source: https://d1.awsstatic.com/r2018/r/Samurai/SamurAI%20Customer%20Assets/Product-Page-Diagram_SamurAI_How-it-works-2.bc19de267c29570783c4add8bb2286ee584fcfbc.png

Annotators

[Google Cloud] Data Labeling

Source: https://cloud.google.com/images/ai-platform/hero-banner_2x.png

Annotators

[Google Cloud] Data Labeling

Source: https://cloud.google.com/images/ai-platform/cloud-ai-platform.svg

Vision

Vision

Source: https://developers.google.com/ml-kit/images/vision/header-vision_1440.png

Vision

[AWS] Rekognition

Source: https://d1.awsstatic.com/product-marketing/Rekognition/Label-detection.b3858b2903ec631bef4603b6689848ed342a4400.png

Vision

[AWS] Rekognition

Source: https://d1.awsstatic.com/product-marketing/Rekognition/custom-labels.dbc1930e54580d2299cc73184f34fa2bdfffe846.png

Vision

[Google Cloud] Vision AI

Source: https://cloud.google.com/images/products/vision/industrial-inspection.svg

Vision

[Azure] Computer Vision

Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/computer-vision/valprop01.svg

Vision

[Azure] Custom Vision

Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/custom-vision-service/value-prop-2-image.png

Vision

[Azure] Form Recognizer

Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/form-recognizer/demo-sample-thumbnails/form-recognizer-demo-sample1.png

Vision

[Azure] Form Recognizer

Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/form-recognizer/demo-sample-thumbnails/form-recognizer-demo-sample5.png

Vision

[Azure] Ink Recognizer

Source: https://azurecomcdn.azureedge.net/cvt-e918f8bc2be525756af58617c14443351ac83a8fd87a0b28d31919d627969098/images/page/services/cognitive-services/ink-recognizer/demo-samples/ink-recognizer-demo-sample1.jpg

Vision

[Azure] Video Indexer

Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/media-services/video-indexer/evolve-user-engagement.png

Text-to-Speech

Text-to-Speech

Source: https://upload.wikimedia.org/wikipedia/commons/thumb/b/b5/TTS_System.svg/1100px-TTS_System.svg.png

Text-to-Speech

[AWS] Polly

Source: https://d1.awsstatic.com/Products/product-name/diagrams/product-page-diagram_Amazon-Polly_Content-Creation.6d4a1496e5698966714a3999c8d88387d7c4dd89.png

Text-to-Speech

[AWS] Polly

Source: https://d1.awsstatic.com/Products/product-name/diagrams/product-page-diagram_Amazon-Polly_eLearning.7987d744e6be4bc811a811f44a9e43dd22367a53.png

Text-to-Speech

[AWS] Polly

Source: https://d1.awsstatic.com/Products/product-name/diagrams/product-page-diagram_Amazon-Polly_Telephony.f8b7fa10d860dcc52deecbb8c4815b37a0d57362.png

Text-to-Speech

[Azure] Text to Speech

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/scenarios/multi-device-conversation.png

Text-to-Speech

[Azure] Text to Speech

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/voice-assistants/overview.png

Speech-to-Text

[Azure] Speech to Text

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/scenarios/conversation-transcription-service.png

Speech-to-Text

[Azure] Speech to Text

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/voice-assistants/task-completion-examples.png

Speech-to-Text

[Azure] Speech to Text

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/voice-assistants/custom-commands-flow.png

Speech-to-Text

[Azure] Speech to Text

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/media/voice-assistants/overview-directlinespeech.png

Speech-to-Text

[Azure] Speaker Recognition

Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/shared/cognitive-services-demos/speaker-verification/president-6.jpg

Image-to-Text

Image-to-Text

Source: https://upload.wikimedia.org/wikipedia/commons/thumb/8/81/Portable_scanner_and_OCR_%28video%29.webm/300px--Portable_scanner_and_OCR_%28video%29.webm.jpg

Image-to-Text

[AWS] Textract

Source: https://d1.awsstatic.com/r2018/r/Hieroglyph/feature-diagram_hieroglyph_OCR.897386f6c5d9c472c423aa8fab9064ed19dc1984.png

Image-to-Text

[AWS] Textract

Source: https://d1.awsstatic.com/r2018/r/Hieroglyph/feature-diagram_hieroglyph_key-value-pair-extraction.673eed38b7860d3c0f295c23e2a10e2fff6a5920.png

Image-to-Text

[AWS] Textract

Source: https://d1.awsstatic.com/r2018/r/Hieroglyph/feature-diagram_hieroglyph_table-extraction.2db019dc23526ca379d6878a779dfcb057193f1a.png

Computational Linguistics

[AWS] Comprehend

Source: https://d1.awsstatic.com/r2018/r/hera/product-page-diagram-AWS-Hera-Launch_How-It-Works@1.5x.78220a4feceef787705be35c1bb772b1b2038b97.png

Computational Linguistics

[AWS] Comprehend

Source: https://d1.awsstatic.com/Comprehend/updated-diagrams/product-page-diagram_Amazon-Comprehend_Voice-Of-Customer.93d8f858f529bec19d1899c9d8a5625d3989e621.png

Computational Linguistics

[AWS] Comprehend

Source: https://d1.awsstatic.com/Comprehend/updated-diagrams/product-page-diagram_Amazon-Comprehend_Semantic-Search.242a46f358903fc05285babc5c35de588313c52d.png

Computational Linguistics

[AWS] Comprehend

Source: https://d1.awsstatic.com/Comprehend/updated-diagrams/product-page-diagram_Amazon-Comprehend_Knowledge-Management-Discovery.24a511406c408bb3e6c921b3446780c76dd461af.png

Computational Linguistics

[AWS] Comprehend

Source: https://d1.awsstatic.com/Comprehend/updated-diagrams/product-page-diagram_Amazon-Comprehend_Customer-Support-Ticket-Handling.77c4681d938bfc004574b95ef9dfa41691b2c3c8.png

Computational Linguistics

[AWS] Comprehend

Source: https://d1.awsstatic.com/r2018/r/hera/product-page-diagram-AWS-Hera-Launch_Clinical-Trial-Recruitment@1.5x.35a35b017bde63978ba29c2364d2507c99920450.png

Computational Linguistics

[Google Cloud] Translate

Source: https://cloud.google.com/images/products/artwork/how-automl-translated-language-works_2x.png

Computational Linguistics

[Google Cloud] Natural Language

Source: https://cloud.google.com/images/natural-language/automl-works.svg

Computational Linguistics

[Azure] Immersive Reader

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/immersive-reader/media/picture-dictionary.png

Computational Linguistics

[Azure] Language Understanding

Source: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/media/information-chatbot.png

Computational Linguistics

[Azure] Language Understanding

Source: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/media/commerce-chatbot.png

Computational Linguistics

[Azure] Language Understanding

Source: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/media/controlling-iot-devices-using-voice.svg

Computational Linguistics

[Azure] QnA Maker

Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/cognitive-services/qna-maker/chit-chat.png

Computational Linguistics

[Azure] QnA Maker

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/media/qnamaker-overview-learnabout/bot-chat-with-qnamaker.png

Computational Linguistics

[Azure] QnA Maker

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/media/qnamaker-overview-learnabout/example-question-and-answer-with-metadata.png

Computational Linguistics

[Azure] QnA Maker

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/media/qnamaker-overview-learnabout/development-cycle.png

Computational Linguistics

[Azure] Text Analytics

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/media/use-cases/survey-results.svg

Computational Linguistics

[Azure] Text Analytics

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/media/use-cases/survey-results.svg

Computational Linguistics

[Azure] Text Analytics

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/media/use-cases/support-incidents.svg

Computational Linguistics

[Azure] Text Analytics

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/media/use-cases/social-feed.svg

Computational Linguistics

[Azure] Text Analytics

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/media/use-cases/sensitive-docs.jpg

AI Apps and Agents (Bots)

[AWS] Lex

Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_Chatbot.b5adc0437cda103fea9ace20fb5f59f4efd57f32.png

AI Apps and Agents (Bots)

[AWS] Lex

Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_InfoBot.57e3395f003ad7be15463ad7cc10d425b9f65e0c.png

AI Apps and Agents (Bots)

[AWS] Lex

Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_ApplicationBot.625139142db01f995e6506698ce04d22610604a8.png

AI Apps and Agents (Bots)

[AWS] Lex

Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_Enterprise.e8867e659d8821dbeffb048b687b479a6d34036d.png

AI Apps and Agents (Bots)

[AWS] Lex

Source: https://d1.awsstatic.com/product-marketing/Lex/lexdiagramsupdated/product-page-diagram_Amazon-Lex_IoT.8e8e02b196e5af5d4b68d891ef3d492839cbb616.png

AI Apps and Agents (Bots)

[Azure] Content Moderator

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/images/content-moderator-mod-api.png

AI Apps and Agents (Bots)

[Azure] Content Moderator

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/content-moderator/images/content-moderator-rev-api.png

AI Apps and Agents (Bots)

[Google Cloud] Dialogflow

Source: https://cloud.google.com/dialogflow/docs/images/console-intent-selected.png

AI-Powered Personalization Services

[AWS] Personalize

Source: https://d1.awsstatic.com/r2018/r/Concierge/product-page-diagram_amazon_personalize_how-it-works.3ceac8883c7d6bd67d7cf26d8a7d505520d02a40.png

AI-Powered Personalization Services

[Google Cloud] Recommendations AI

Source: https://cloud.google.com/images/recommendations/workflow-diagram.svg?dcb_=0.8029302210032698

AI-Powered Personalization Services

[Azure] Personalizer

Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/cognitive-services/personalizer/market-quickly.png

AI-Powered Anomaly and Fraud Detector Services

[AWS] Fraud Detector

Source: https://d1.awsstatic.com/re19/Hawksnest/trigger_rules.ed9d347d3c009622353611805f9557b6a3030e0f.png

AI-Powered Anomaly and Fraud Detector Services

[Azure] Anomaly Detector

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/anomaly-detector/media/anomaly_detection2.png

AI-Powered Anomaly and Fraud Detector Services

[Azure] Anomaly Detector

Source: https://azurecomcdn.azureedge.net/cvt-1d5d83e09d2171261e69bc5faba5f3018c1bf1c590f0e666d26c9bc9ef7a561b/images/page/services/cognitive-services/anomaly-detector/diagram-speed-time-insights.png

AI-Powered Forecast Services

[AWS] Forecast

Source: https://d1.awsstatic.com/r2018/r/seer/diagrams/Seer_HowitWorks_Final.44b02658b17d05e9242b450b220f6e0ca4065638.png

AI-Powered Software Intelligence

[AWS] CodeGuru

Source: https://d1.awsstatic.com/re19/Screenshot_FIND-YOUR-MOST_Annotated.f36905ca4327545f5f01f475f5610336ef889185.png

AI-Powered Software Intelligence

[AWS] CodeGuru

Source: https://d1.awsstatic.com/re19/Screenshot_Catch-Code-Issue_2%20-%20Annotations%20LP.df0deb64bbfb02219d429db4d5bd3efd089e2a89.png

AI-Powered Search Services

[AWS] Kendra

Source: https://d1.awsstatic.com/re19/Kendra/WEB_Kendra_Before-After.f62b81392674c05785f547c731a4a087faabf412.png

AI-Powered Search Services

[Azure] Bing Custom Search

Source: https://docs.microsoft.com/en-us/azure/cognitive-services/Bing-Custom-Search/media/bcs-overview.png