Machine Learning (ML) and Artificial Intelligence (AI)

AWS helps you at every stage of your ML adoption journey with the most comprehensive set ML services and purpose-built infrastructure. Our pretrained AI services provide ready-made intelligence for your applications and workflows.

Each service is described after the diagram. To help you decide which service best meets your needs, see Choosing an AWS machine learning service, Choosing a generative AI service, and Amazon Bedrock or Amazon SageMaker?. For general information, see Build and scale the next wave of AI innovation on AWS.

Diagram showing AWS artificial intelligence stack

Return to AWS services.

Amazon Augmented AI

Amazon Augmented AI (Amazon A2I) is a ML service which makes it easy to build the workflows required for human review. 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, whether it runs on AWS or not.

Amazon Bedrock

Amazon Bedrock is a fully managed service that makes foundational models (FMs) from Amazon and leading AI startups available through an API. With the Amazon Bedrock serverless experience, you can quickly get started, experiment with FMs, privately customize them with your own data, and seamlessly integrate and deploy FMs into your AWS applications.

You can choose from a variety of foundation models, including Amazon Titan, Claude 2 from Anthropic, Command and Embed from Cohere, Jurassic-2 from AI21 Studio, and Stable Diffusion from Stability AI.

Amazon CodeGuru

Amazon CodeGuru is a developer tool that provides intelligent recommendations to improve code quality and identify an application’s most expensive lines of code. Integrate CodeGuru into your existing software development workflow to automate code reviews during application development and continuously monitor application's performance in production and provide recommendations and visual clues on how to improve code quality, application performance, and reduce overall cost.

Amazon CodeGuru Reviewer uses ML and automated reasoning to identify critical issues, security vulnerabilities, and hard-to-find bugs during application development and provides recommendations to improve code quality.

Amazon CodeGuru Profiler helps developers find an application’s most expensive lines of code by helping them understand the runtime behavior of their applications, identify and remove code inefficiencies, improve performance, and significantly decrease compute costs.

Amazon Comprehend

Amazon Comprehend uses ML and natural language processing (NLP) 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.

For extracting complex medical information from unstructured text, you can use Amazon Comprehend Medical. The service can identify medical information, such as medical conditions, medications, dosages, strengths, and frequencies from a variety of sources like doctor’s notes, clinical trial reports, and patient health records. Amazon Comprehend Medical also identifies the relationship among the extracted medication and test, treatment and procedure information for easier analysis. For example, the service identifies a particular dosage, strength, and frequency related to a specific medication from unstructured clinical notes.

Amazon DevOps Guru

Amazon DevOps Guru is an ML-powered service that makes it easy to improve an application’s operational performance and availability. Amazon DevOps Guru detects behaviors that deviate from normal operating patterns so you can identify operational issues long before they impact your customers.

Amazon DevOps Guru uses ML models informed by years of Amazon.com and AWS operational excellence to identify anomalous application behavior (such as increased latency, error rates, resource constraints, etc.) and surface critical issues that could cause potential outages or service disruptions. When Amazon DevOps Guru identifies a critical issue, it automatically sends an alert and provides a summary of related anomalies, the likely root cause, and context about when and where the issue occurred. When possible, Amazon DevOps Guru also provides recommendations on how to remediate the issue.

Amazon DevOps Guru automatically ingests operational data from your AWS applications and provides a single dashboard to visualize issues in your operational data. You can get started by enabling Amazon DevOps Guru for all resources in your AWS account, resources in your AWS CloudFormation Stacks, or resources grouped together by AWS tags, with no manual setup or ML expertise required.

Amazon Forecast

Amazon Forecast is a fully managed service that uses ML 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 such as product features and store locations.

Based on the same technology used at Amazon.com, Amazon Forecast uses ML to combine time series data with additional variables to build forecasts. Amazon Forecast requires no ML 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 ML 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 ML models to build, train, or deploy. You pay only for what you use, and there are no minimum fees and no upfront commitments.

Amazon Fraud Detector

Amazon Fraud Detector is a fully managed service that uses ML and more than 20 years of fraud detection expertise from Amazon, to identify potentially fraudulent activity so customers can catch more online fraud faster. Amazon Fraud Detector automates the time consuming and expensive steps to build, train, and deploy an ML model for fraud detection, making it easier for customers to leverage the technology. Amazon Fraud Detector customizes each model it creates to a customer’s own dataset, making the accuracy of models higher than current one-size fits all ML solutions. And, because you pay only for what you use, you avoid large upfront expenses.

Amazon Comprehend Medical

Over the past decade, AWS has witnessed a digital transformation in health, with organizations capturing huge volumes of patient information every day. But this data is often unstructured and the process to extract this information is labor-intensive and error-prone. Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning that has been pre-trained to understand and extract health data from medical text, such as prescriptions, procedures, or diagnoses. Amazon Comprehend Medical can help you extract information from unstructured medical text accurately and quickly with medical ontologies like ICD-10-CM, RxNorm, and SNOMED CT and in turn accelerate insurance claim processing, improve population health, and accelerate pharmacovigilance.

Amazon Kendra

Amazon Kendra is an intelligent search service powered by ML. Amazon Kendra reimagines enterprise search for your websites and applications so your employees and customers can easily find the content they are looking for, even when it’s scattered across multiple locations and content repositories within your organization.

Using Amazon Kendra, you can stop searching through troves of unstructured data and discover the right answers to your questions, when you need them. Amazon Kendra is a fully managed service, so there are no servers to provision, and no ML models to build, train, or deploy.

Amazon Lex

Amazon Lex is a fully managed artificial intelligence (AI) service to design, build, test, and deploy conversational interfaces into any application using voice and text. 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, and create new categories of products. 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”) and voice enabled interactive voice response (IVR) systems.

Amazon Lex enables developers to build conversational chatbots quickly. With Amazon Lex, no deep learning expertise is necessary—to create a bot, you just specify the basic conversation flow in the Amazon Lex console. Amazon Lex manages the dialogue and dynamically adjusts the responses in the conversation. Using the console, you can build, test, and publish your text or voice chatbot. You can then add the conversational interfaces to bots on mobile devices, web applications, and chat platforms (for example, Facebook Messenger). There are no upfront costs or minimum fees to use Amazon Lex - you are charged only for the text or speech requests that are made. The pay-as-you-go pricing and the low cost per request make the service a cost-effective way to build conversational interfaces. With the Amazon Lex free tier, you can easily try Amazon Lex without any initial investment.

Amazon Lookout for Equipment

Amazon Lookout for Equipment analyzes the data from the sensors on your equipment (such as pressure in a generator, flow rate of a compressor, revolutions per minute of fans), to automatically train an ML model based on just your data, for your equipment – with no ML expertise required. Lookout for Equipment uses your unique ML model to analyze incoming sensor data in real-time and accurately identify early warning signs that could lead to machine failures. This means you can detect equipment abnormalities with speed and precision, quickly diagnose issues, take action to reduce expensive downtime, and reduce false alerts.

Amazon Lookout for Metrics

Amazon Lookout for Metrics uses ML to automatically detect and diagnose anomalies (outliers from the norm) in business and operational data, such as a sudden dip in sales revenue or customer acquisition rates. In a couple of clicks, you can connect Amazon Lookout for Metrics to popular data stores such as Amazon S3, Amazon Redshift, and Amazon Relational Database Service (Amazon RDS), as well as third-party Software as a Service (SaaS) applications, such as Salesforce, Servicenow, Zendesk, and Marketo, and start monitoring metrics that are important to your business. Amazon Lookout for Metrics automatically inspects and prepares the data from these sources to detect anomalies with greater speed and accuracy than traditional methods used for anomaly detection. You can also provide feedback on detected anomalies to tune the results and improve accuracy over time. Amazon Lookout for Metrics makes it easy to diagnose detected anomalies by grouping together anomalies that are related to the same event and sending an alert that includes a summary of the potential root cause. It also ranks anomalies in order of severity so that you can prioritize your attention to what matters the most to your business.

Amazon Lookout for Vision

Amazon Lookout for Vision is an ML service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. For example, Amazon Lookout for Vision can be used to identify missing components in products, damage to vehicles or structures, irregularities in production lines, miniscule defects in silicon wafers, and other similar problems. Amazon Lookout for Vision uses ML to see and understand images from any camera as a person would, but with an even higher degree of accuracy and at a much larger scale. Amazon Lookout for Vision allows customers to eliminate the need for costly and inconsistent manual inspection, while improving quality control, defect and damage assessment, and compliance. In minutes, you can begin using Amazon Lookout for Vision to automate inspection of images and objects – with no ML expertise required.

Amazon Monitron

Amazon Monitron is an end-to-end system that uses ML to detect abnormal behavior in industrial machinery, enabling you to implement predictive maintenance and reduce unplanned downtime.

Installing sensors and the necessary infrastructure for data connectivity, storage, analytics, and alerting are foundational elements for enabling predictive maintenance. However, to make it work, companies have historically needed skilled technicians and data scientists to piece together a complex solution from scratch. This included identifying and procuring the right type of sensors for their use cases and connecting them together with an IoT gateway (a device that aggregates and transmits data). As a result, few companies have been able to successfully implement predictive maintenance.

Amazon Monitron includes sensors to capture vibration and temperature data from equipment, a gateway device to securely transfer data to AWS, the Amazon Monitron service that analyzes the data for abnormal machine patterns using ML, and a companion mobile app to set up the devices and receive reports on operating behavior and alerts to potential failures in your machinery. You can start monitoring equipment health in minutes without any development work or ML experience required, and enable predictive maintenance with the same technology used to monitor equipment in Amazon Fulfillment Centers.

Amazon PartyRock

Amazon PartyRock makes learning generative AI easy with a hands-on, code-free app builder. Experiment with prompt engineering techniques, review generated responses, and develop intuition for generative AI while creating and exploring fun apps. PartyRock provides access to foundation models (FMs) from Amazon and leading AI companies through Amazon Bedrock, a fully managed serviced service.

Amazon Personalize

Amazon Personalize is an ML service that makes it easy for developers to create individualized recommendations for customers using their applications.

ML is increasingly used to improve customer engagement by powering personalized product and content recommendations, tailored search results, and targeted marketing promotions. However, developing the ML capabilities necessary to produce these sophisticated recommendation systems has been beyond the reach of most organizations today due to the complexity of developing ML functionality. Amazon Personalize allows developers with no prior ML experience to easily build sophisticated personalization capabilities into their applications, using ML technology perfected from years of use on Amazon.com.

With Amazon Personalize, you provide an activity stream from your application – 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 processes and examines the data, identifies what is meaningful, selects the right algorithms, and trains and optimizes a personalization model that is customized for your data.

Amazon Personalize offers optimized recommenders for retail and media and entertainment that make it faster and easier to deliver high-performing personalized user experiences. Amazon Personalize also offers intelligent user segmentation so you can run more effective prospecting campaigns through your marketing channels. With our two new recipes, you can automatically segment your users based on their interest in different product categories, brands, and more.

All data analyzed by Amazon Personalize is kept private and secure, and only used for your customized recommendations. You can start serving your personalized predictions via a simple API call from inside the virtual private cloud that the service maintains. 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 ML personalization team at your disposal, 24 hours a day.

Amazon Polly

Amazon Polly is a service that turns text into lifelike speech. Amazon Polly lets you create applications that talk, enabling you to build entirely new categories of speech-enabled products. Amazon Polly is an Amazon artificial intelligence (AI) service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice. Amazon Polly includes a wide selection of lifelike voices spread across dozens of languages, so you can select the ideal voice and build speech-enabled applications that work in many different countries.

Amazon Polly delivers the consistently fast response times required to support real-time, interactive dialog. You can cache and save Amazon Polly speech audio to replay offline or redistribute. And Amazon Polly is easy to use. You simply send the text you want converted into speech to the Amazon Polly API, and Amazon Polly immediately returns the audio stream to your application so your application can play it directly or store it in a standard audio file format, such as MP3.

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 a Newscaster speaking style that is tailored to news narration use cases. 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.

With Amazon Polly, you pay only for the number of characters you convert to speech, and you can save and replay Amazon Polly generated speech. The Amazon Polly low cost per character converted, and lack of restrictions on storage and reuse of voice output, make it a cost-effective way to enable Text-to-Speech everywhere.

Amazon Q

Amazon Q is a generative AI-powered assistant for accelerating software development and leveraging your internal data.

Amazon Rekognition

Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no ML 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 ML experience is required. You simply need to supply images of objects or scenes you want to identify, and the service handles the rest.

Amazon SageMaker

With Amazon SageMaker, you can build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows. SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. SageMaker provides all of the components used for ML in a single toolset so models get to production faster with much less effort and at lower cost.

Amazon SageMaker Autopilot

Amazon SageMaker Autopilot automatically builds, trains, and tunes the best ML models based on your data, while allowing you to maintain full control and visibility. With SageMaker Autopilot, you simply 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.

Amazon SageMaker Canvas

Amazon SageMaker Canvas expands access to ML by providing business analysts with a visual point-and-click interface that allows them to generate accurate ML predictions on their own — without requiring any ML experience or having to write a single line of code.

Amazon SageMaker Clarify

Amazon SageMaker Clarify provides machine learning developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions. Amazon SageMaker Clarify detects potential bias during data preparation, after model training, and in your deployed model by examining attributes you specify. SageMaker Clarify also includes feature importance graphs that help you explain model predictions and produces reports which can be used to support internal presentations or to identify issues with your model that you can take steps to correct.

Amazon SageMaker Data Labeling

Amazon SageMaker provides data labeling offerings to identify raw data, such as images, text files, and videos, and add informative labels to create high-quality training datasets for your ML models.

Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for ML from weeks to minutes. With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering, and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface.

Amazon SageMaker Edge

Amazon SageMaker Edge enables machine learning on edge devices by optimizing, securing, and deploying models to the edge, and then monitoring these models on your fleet of devices, such as smart cameras, robots, and other smart-electronics, to reduce ongoing operational costs. SageMaker Edge Compiler optimizes the trained model to be runnable on an edge device. SageMaker Edge includes an over-the-air (OTA) deployment mechanism that helps you deploy models on the fleet independent of the application or device firmware. SageMaker Edge Agent allows you to run multiple models on the same device. The Agent collects prediction data based on the logic that you control, such as intervals, and uploads it to the cloud so that you can periodically retrain your models over time.

Amazon SageMaker Feature Store

Amazon SageMaker Feature Store is a purpose-built repository where you can store and access features so it’s much easier to name, organize, and reuse them across teams. SageMaker Feature Store provides a unified store for features during training and real-time inference without the need to write additional code or create manual processes to keep features consistent. SageMaker Feature Store keeps track of the metadata of stored features (such as feature name or version number) so that you can query the features for the right attributes in batches or in real time using Amazon Athena, an interactive query service. SageMaker Feature Store also keeps features updated, because as new data is generated during inference, the single repository is updated so new features are always available for models to use during training and inference.

Amazon SageMaker geospatial capabilities

Amazon SageMaker geospatial capabilities make it easier for data scientists and machine learning (ML) engineers to build, train, and deploy ML models faster using geospatial data. You have access to data (open-source and third-party), processing, and visualization tools to make it more efficient to prepare geospatial data for ML. You can increase your productivity by using purpose-built algorithms and pre-trained ML models to speed up model building and training, and use built-in visualization tools to explore prediction outputs on an interactive map and then collaborate across teams on insights and results.

Amazon SageMaker HyperPod

Amazon SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for large language models (LLMs), diffusion models, and foundation models (FMs). SageMaker HyperPod is pre-configured with distributed training libraries that enable customers to automatically split training workloads across thousands of accelerators, such as AWS Trainium, and NVIDIA A100 and H100 Graphical Processing Units (GPUs).

SageMaker HyperPod also helps ensure that you can continue training uninterrupted by periodically saving checkpoints. When a hardware failure occurs, self-healing clusters automatically detect the failure, repair or replace the faulty instance, and resume the training from the last saved checkpoint, removing the need for you to manually manage this process and helping you train for weeks or months in a distributed setting without disruption. You can customize your computing environment to best suit your needs and configure it with the Amazon SageMaker distributed training libraries to achieve optimal performance on AWS.

Amazon SageMaker JumpStart

Amazon SageMaker JumpStart helps you quickly and easily get started with ML. To make it easier to get started, SageMaker JumpStart provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks. The solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey. Amazon SageMaker JumpStart also supports one-click deployment and fine-tuning of more than 150 popular open-source models such as natural language processing, object detection, and image classification models.

Amazon SageMaker Model Building

Amazon SageMaker provides all the tools and libraries you need to build ML models, the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case. In Amazon SageMaker you can pick different algorithms, including over 15 that are built-in and optimized for SageMaker, and use over 750 pre-built models from popular model zoos available with a few clicks. SageMaker also offers a variety of model building tools, including Amazon SageMaker Studio Notebooks, JupyterLab, RStudio, and Code Editor based on Code-OSS (Virtual Studio Code Open Source), where you can run ML models on a small scale to see results and view reports on their performance so you can come up with high-quality working prototypes.

Amazon SageMaker Model Training

Amazon SageMaker reduces the time and cost to train and tune ML models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, you can use the Amazon SageMaker distributed training libraries for better performance or use third-party libraries such as DeepSpeed, Horovod, or Megatron.

Amazon SageMaker Model Deployment

Amazon SageMaker makes it easy to deploy ML models to make predictions (also known as inference) at the best price-performance for any use case. It provides a broad selection of ML infrastructure and model deployment options to help meet all your ML inference needs. It is a fully managed service and integrates with MLOps tools, so you can scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden.

Amazon SageMaker Pipelines

Amazon SageMaker Pipelines is the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for ML. With SageMaker Pipelines, you can create, automate, and manage end-to-end ML workflows at scale.

Amazon SageMaker Studio Lab

Amazon SageMaker Studio Lab is a free ML development environment that provides the compute, storage (up to 15GB), and security—all at no cost—for anyone to learn and experiment with ML. All you need to get started is a valid email address—you don’t need to configure infrastructure or manage identity and access or even sign up for an AWS account. SageMaker Studio Lab accelerates model building through GitHub integration, and it comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately. SageMaker Studio Lab automatically saves your work so you don’t need to restart in between sessions. It’s as easy as closing your laptop and coming back later.

Apache MXNet on AWS

Apache MXNet is a fast and scalable training and inference framework with an easy-to-use, concise API for ML. 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 onAWS with a fully-managed experience using Amazon SageMaker, a platform to build, train, and deploy ML models at scale. Or, you can use the AWS Deep Learning AMIss to build custom environments and workflows with MxNet as well as other frameworks including TensorFlow, PyTorch, Chainer, Keras, Caffe, Caffe2, and Microsoft Cognitive Toolkit.

AWS Deep Learning AMIss

The AWS Deep Learning AMIs provide ML 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.

AWS Deep Learning Containers

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, 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.

Geospatial ML with Amazon SageMaker

Amazon SageMaker geospatial capabilities allow data scientists and ML engineers to build, train, and deploy ML models using geospatial data faster and at scale. You can access readily available geospatial data sources, efficiently transform or enrich large-scale geospatial datasets with purpose-built operations, and accelerate model building by selecting pretrained ML models. You can also analyze geospatial data and explore model predictions on an interactive map using 3D accelerated graphics with built-in visualization tools. SageMaker Runtime geospatial capabilities can be used for a wide range of use cases, such as maximizing harvest yield and food security, assessing risk and insurance claims, supporting sustainable urban development, and forecasting retail site utilization.

Hugging Face on AWS

With Hugging Face on Amazon SageMaker, you can deploy and fine-tune pre-trained models from Hugging Face, an open-source provider of natural language processing (NLP) models known as Transformers, reducing the time it takes to set up and use these NLP models from weeks to minutes. NLP refers to ML algorithms that help computers understand human language. They help with translation, intelligent search, text analysis, and more. However, NLP models can be large and complex (sometimes consisting of hundreds of millions of model parameters), and training and optimizing them requires time, resources, and skill. AWS collaborated with Hugging Face to create Hugging Face AWS Deep Learning Containers (DLCs), which provide data scientists and ML developers a fully managed experience for building, training, and deploying state-of-the-art NLP models on Amazon SageMaker.

PyTorch on AWS

PyTorch is an open-source deep learning framework that makes it easy to develop machine learning models and deploy them to production. Using TorchServe, PyTorch's model serving library built and maintained by AWS in partnership with Facebook, PyTorch developers can quickly and easily deploy models to production. PyTorch also provides dynamic computation graphs and libraries for distributed training, which are tuned for high performance on AWS. You can get started with PyTorch on AWS using Amazon SageMaker, a fully managed ML service that makes it easy and cost-effective to build, train, and deploy PyTorch models at scale. If you prefer to manage the infrastructure yourself, you can use the AWS Deep Learning AMIss or the AWS Deep Learning Containers, which come built from source and optimized for performance with the latest version of PyTorch to quickly deploy custom machine learning environments.

TensorFlow on AWS

TensorFlow is one of many deep learning frameworks available to researchers and developers to enhance their applications with machine learning. AWS provides broad support for TensorFlow, enabling customers to develop and serve their own models across computer vision, natural language processing, speech translation, and more. You can get started with TensorFlow on AWS using Amazon SageMaker, a fully managed ML service that makes it easy and cost-effective to build, train, and deploy TensorFlow models at scale. If you prefer to manage the infrastructure yourself, you can use the AWS Deep Learning AMIss or the AWS Deep Learning Containers, which come built from source and optimized for performance with the latest version of TensorFlow to quickly deploy custom ML environments.

Amazon Textract

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.

Today, many companies manually extract data from scanned documents such as PDFs, images, tables, and forms, or through simple OCR software that requires manual configuration (which often must be updated when the form changes). To overcome these manual and expensive processes, Amazon Textract uses ML to read and process any type of document, accurately extracting text, handwriting, tables, and other data with no manual effort. Amazon Textract provides you with the flexibility to specify the data you need to extract from documents using queries. You can specify the information you need in the form of natural language questions (such as “What is the customer name”). You do not need to know the data structure in the document (table, form, implied field, nested data) or worry about variations across document versions and formats. Amazon Textract Queries are pre-trained on a large variety of documents including paystubs, bank statements, W-2s, loan application forms, mortgage notes, claims documents, and insurance cards.

With Amazon Textract, you can quickly automate document processing and act on the information extracted, whether you’re automating loans processing or extracting information from invoices and receipts. Amazon Textract can extract the data in minutes instead of hours or days. Additionally, you can add human reviews with Amazon Augmented AI to provide oversight of your models and check sensitive data.

Amazon Transcribe

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for customers to automatically convert speech to text. The service can transcribe audio files stored in common formats, like WAV and MP3, with time stamps for every word so that you can easily locate the audio in the original source by searching for the text. You can also send a live audio stream to Amazon Transcribe and receive a stream of transcripts in real time. Amazon Transcribe is designed to handle a wide range of speech and acoustic characteristics, including variations in volume, pitch, and speaking rate. The quality and content of the audio signal (including but not limited to factors such as background noise, overlapping speakers, accented speech, or switches between languages within a single audio file) may affect the accuracy of service output. Customers can choose to use Amazon Transcribe for a variety of business applications, including transcription of voice-based customer service calls, generation of subtitles on audio/video content, and conduct (text based) content analysis on audio/video content.

Two very important services derived from Amazon Transcribe include Amazon Transcribe Medical and Amazon Transcribe Call Analytics.

Amazon Transcribe Medical uses advanced ML models to accurately transcribe medical speech into text. Amazon Transcribe Medical can generate text transcripts that can be used to support a variety of use cases, spanning clinical documentation workflow and drug safety monitoring (pharmacovigilance) to subtitling for telemedicine and even contact center analytics in the healthcare and life sciences domains.

Amazon Transcribe Call Analytics is an AI-powered API that provides rich call transcripts and actionable conversation insights that you can add into their call applications to improve customer experience and agent productivity. It combines powerful speech-to-text and custom natural language processing (NLP) models that are trained specifically to understand customer care and outbound sales calls. As a part of AWS Contact Center Intelligence (CCI) solutions, this API is contact center agnostic and makes it easy for customers and ISVs to add call analytics capabilities into their applications.

The easiest way to get started with Amazon Transcribe is to submit a job using the console to transcribe an audio file. You can also call the service directly from the AWS Command Line Interface, or use one of the supported SDKs of your choice to integrate with your applications.

Amazon Translate

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 your diverse users, easily translate large volumes of text for analysis, and efficiently enable cross-lingual communication between users.

AWS DeepComposer

AWS DeepComposer is the world’s first musical keyboard powered by ML to enable developers of all skill levels to learn Generative AI while creating original music outputs. DeepComposer consists of a USB keyboard that connects to the developer’s computer, and the DeepComposer service, accessed through the AWS Management Console. DeepComposer includes tutorials, sample code, and training data that can be used to start building generative models.

AWS DeepRacer

AWS DeepRacer is a 1/18^th scale race car which gives you an interesting and fun way to get started with reinforcement learning (RL). RL is an advanced ML technique which takes a very different approach to training models than other ML methods. Its superpower is that it learns very complex behaviors without requiring any labeled training data, and can make short term decisions while optimizing for a longer term goal.

With AWS DeepRacer, you now have a way to get hands-on with RL, experiment, and learn through autonomous driving. You can get started with the virtual car and tracks in the cloud-based 3D racing simulator, and for a real-world experience, you can deploy your trained models onto AWS DeepRacer and race your friends, or take part in the global AWS DeepRacer League. Developers, the race is on.

AWS HealthLake

AWS HealthLake is a HIPAA-eligible service that healthcare providers, health insurance companies, and pharmaceutical companies can use to store, transform, query, and analyze large-scale health data.

Health data is frequently incomplete and inconsistent. It's also often unstructured, with information contained in clinical notes, lab reports, insurance claims, medical images, recorded conversations, and time-series data (for example, heart ECG or brain EEG traces).

Healthcare providers can use HealthLake to store, transform, query, and analyze data in the AWS Cloud. Using the HealthLake integrated medical natural language processing (NLP) capabilities, you can analyze unstructured clinical text from diverse sources. HealthLake transforms unstructured data using natural language processing models, and provides powerful query and search capabilities. You can use HealthLake to organize, index, and structure patient information in a secure, compliant, and auditable manner.

AWS HealthScribe

AWS HealthScribe is a HIPAA-eligible service that allows healthcare software vendors to automatically generate clinical notes by analyzing patient-clinician conversations. AWS HealthScribe combines speech recognition with generative AI to reduce the burden of clinical documentation by transcribing conversations and quickly producing clinical notes. Conversations are segmented to identify the speaker roles for patients and clinicians, extract medical terms, and generate preliminary clinical notes. To protect sensitive patient data, security and privacy are built-in to ensure that the input audio and the output text are not retained in AWS HealthScribe.

AWS Panorama

AWS Panorama is a collection of ML devices and software development kit (SDK) that brings computer vision (CV) to on-premises internet protocol (IP) cameras. With AWS Panorama, you can automate tasks that have traditionally required human inspection to improve visibility into potential issues.

Computer vision can automate visual inspection for tasks such as tracking assets to optimize supply chain operations, monitoring traffic lanes to optimize traffic management, or detecting anomalies to evaluate manufacturing quality. In environments with limited network bandwidth however, or for companies with data governance rules that require on-premises processing and storage of video, computer vision in the cloud can be difficult or impossible to implement. AWS Panorama is an ML service that allows organizations to bring computer vision to on-premises cameras to make predictions locally with high accuracy and low latency.

The AWS Panorama Appliance is a hardware device that adds computer vision to your existing IP cameras and analyzes the video feeds of multiple cameras from a single management interface. It generates predictions at the edge in milliseconds, meaning you can be notified about potential issues such as when damaged products are detected on a fast-moving production line, or when a vehicle has strayed into a dangerous off-limits zone in a warehouse. And, third-party manufacturers are building new AWS Panorama-enabled cameras and devices to provide even more form factors for your unique use cases. With AWS Panorama you can use ML models from AWS to build your own computer vision applications, or work with a partner from the AWS Partner Network to build CV applications quickly.

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