Amazon Machine Learning (ML) and Artifical Intelligence (AI)

Textract

a service that automatically extracts text and data from scanned documents; goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables; simple OCR software is less powerful and still requires manual configuration which must be updated as forms change; this service overcomes these challenges using ML to read and process any type of document with no manual effort; provides you with the flexibility to specify the data you need to extract from documents using queries; specify the information you need in the form of natural language questions (e.g. "What is the customer name?"); you do not need to know the data structure in the document or worry about variations across document versions and formats; Queries are pre-trained on a large variety of documents including paystubs, bank statements, W2s, loan application forms, mortgage notes, claims documents and insurance cards; quickly automate document processing and act on the information extracted, whether you're automating loans processing or extracting information from invoices and receipts; add human reviews with Augmented AI to provide oversight of models and check sensitive data (if needed)

Polly

a service that turns text into lifelike speech; lets you create applications that talk, enabling you to build entirely new categories of speech-enabled products; an AI service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice; includes a wide selection of lifelike voices spread across dozens of languages so you can select the ideal voice and build speech-enables applications that work in many different countries; delivers the consistently fast response times required to support real-time, interactive dialogue; cache and save speech audio to replay offline or redistribute; easy to use, simply send the text you want converted into speech to the API and immediately return the audio stream to the application so it can be played directly or stored in a standard audio file format such as MP3; also offers Neural Text-to-Speech (NTTS) voices that deliver advanced improvements in speech quality through a new machine learning approach; NTTS supports a newscaster speaking style that is tailored to news narration use cases; Polly Brand Voice can also create a custom voice for your organization by collaboration with the Amazon Polly team; pay only for the number of characters you convert to speech; with no restrictions on storage and reuse of voice output

Geospatial ML with SageMaker

allow data scientists and ML engineers to build, train and deploy ML models using geospatial data faster and at scale; access readily available geospatial data sources, efficiently transform or enrich large-scale datasets with purpose-built operations and accelerate model building by selecting pretrained models; analyze geospatial data and explore model predictions on an interactive map using 3D accelerated graphics with built-in visualization tools; can be used for a wide variety 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

Transcribe Call Analytics

an AI powered API that provides rich call transcripts and actionable conversation insights that you can add into call applications to improve customer experience and agent productivity; combines powerful speech to text and custom NLP models that are trained specifically to understand customer care and outbound sales calls; as a part of AWS Context 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

Deep Learning Containers

Docker images pre-installed with deep learning frameworks to make it easy to deploy custom ML environments quickly by letting you skip the complicated process of building and optimizing your environments from scratch; supports TensorFlow, PyTorch, Apache MXNet; deploy containers on SageMaker, EKS, self-managed k8s on EC2, ECS; containers are available through ECR and Marketplace at no additional cost - pay only for the resources you use

DeepRacer

a 1/8th scale race car used to get started with reinforcement leaning (RL); RL is an advanced ML technique which takes a very different approach to training models than other methods; its superpower is that is learns very complex behaviors without requiring any labeled training data and can make short term decisions while optimizing for a longer term goal; get hands-on experience with RL, experiment and learn through autonomous driving; get started with the virtual car and tracks in the cloud-based 3D racing simulator and deploy your trained models onto DeepRacer and race your friends or take part in the global DeepRacer league

Comprehend Medical

a HIPAA eligible NLP service that uses ML that has been pre-trained to understand and extract health data from medical text, such as prescriptions, procedures, or diagnoses; helps to extract information from unstructured text accurately and quickly with medical ontologies like ICD-10-CM, RxNorm, and SNOMED CT to accelerate insurance claim processing , improve population health and accelerate pharmacovigilance

HealthLake

a HIPAA eligible service that healthcare providers, insurance companies, pharma companies can use to store, transform, query and analyze large-scale health data; health data is frequently incomplete and inconsistent, unstructured with information contained in clinical notes, lab reports, claims, medical images, recorded conversations and time-series data (heart ECG, brain EEG, etc.); using the integrated medical NLP capabilities, analyze this unstructured data from various sources; NLP models are used to transform the data so that you can query and search the data using this service; organize, index, structure patient information in a secure, compliant and auditable manner

HealthScribe

a HIPPA eligible service that allows healthcare software vendors to automatically generate clinical notes by analyzing patient-clinician conversations; 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 notes; security and privacy are built-in to ensure that the input audio and output text are not retained by the managed service

Augmented AI

a ML service which makes it easy to build the workflows required for human review; 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

Panorama

a collection of ML devices and software development kit that brings computer vision to on-prem internet protocol (IP) cameras; automate tasks that have traditionally required human inspection to improve visibility into potential issues (tracking assets to optimize supply chain ops, monitoring traffic lanes to optimize management, detecting anomalies to evaluate manufacturing quality); in cases where network bandwidth is limited or data governance requires on-prem video processing, computer vision in the cloud can be difficult to implement; the Panorama Appliance is a hardware device that adds computer vision to existing cameras and analyzes the feeds of multiple cameras from a single interface; it generates predictions at the edge in milliseconds for rapid notification of potential issues; third-party manufacturers are building new Panorama-enables cameras and devices to provide even more form factors to unique use cases; use ML models to build your own computer vision applications or work with the AWS partner network to build them

CodeGuru

a developer tool that provides intelligent recommendations to improve code quality and identify an application's most expensive lines of code; integrate into your existing software development workflow to automate code reviews 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; the Reviewer feature uses ML and automated reasoning to identify critical issues, security vulnerabilities and hard-to-find bugs during application development; the Profiler feature helps developers find the most expensive lines of code by helping them understand the runtime behavior of their applications, identify and remove code inefficiencies, improve performance and significantly reduce compute costs

Apache MXNet on AWS

a fast and scalable training and inference framework with an easy-to-use, concise API for ML; 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, build linear regression, convolutional networks and recurrent LSTMs for object detection, speech recognition, recommendation and personalization; for a fully managed experience, use in conjunction with SageMaker, or 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

SageMaker Studio Lab

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 that is needed to get started is a valid email address, no need to configure infrastructure or manage identity and access or even sign up for an AWS account; accelerates model building through GitHub integration and comes preconfigured with the most popular ML tools, frameworks, and libraries to get you started immediately; automatically saves your work so you don't need to restart in between sessions

Lex

a fully managed AI service to design, build, test and deploy conversational interfaces into any application using voice and text; 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; the same deep learning technologies that power Alexa are used, enabling you to quickly and easily build sophisticated, natural language, conversational bots and voice enabled interactive response (IVR) systems; no deep learning expertise required, specify basic conversation flow in the console; Lex manages the dialogue and dynamically adjusts the responses in the conversation; build, test, and publish your text or voice chatbot in the console, then add the interfaces to bots on mobile devices, web applications, and chat platforms (e.g. Messenger); no upfront costs or minimum fees, charge only for the number of requests made; free tier available to test without any initial investment

Bedrock

a fully managed service that makes foundational models (FMs) from Amazon and leading AI startups available through an API; with the serverless experience, quickly get started, experiment with FMs privately customize them with your own data and seamlessly integrate and deploy FMs into your AWS applications; choose from a variety of FMs including Amazon Titan, Claude 2 from Anthropic, Command and Embed from Cohere, Jurassic-2 from AI21 Studio, and Stable Diffusion from Stability AI

Fraud Detector

a fully managed service that uses ML and more than 20 years of fraud detection expertise from Amazon to identify potentially fraudulent activity; automates the time consuming and expensive steps to build, train and deploy an ML model; each model is customized to the customer's own dataset, making the accuracy of the models higher than the current one size fits all ML solutions; pay only for what you use, no upfront costs

Forecast

a fully-managed service that uses ML to deliver highly accurate forecasts; forecasts are typically used by business to predict outcomes such as product demand, resource needs, financial performance, etc. using historical time-series data (has limitations with irregular trends and fails to take into account changes over time to things like price, web traffic, discounts etc.); Forecast uses ML to combine time series data with additional variables to build forecasts more accurately; requires no ML experience to get started, service is based on the same technology used by Amazon; provide historical data, plus any additional data that may impact forecasts (e.g. the color of shirt may change with the seasons and store location); once data is provided, it will be examined and meaningful insights will be identified creating forecasts with up to 50% more accuracy than time-series data alone produce; no server provisioning, model building/training required, pay only for what you use with no minimums or upfront costs

Translate

a neural machine translation service that delivers fast, high-quality and affordable language translation; neural machine translation is a form of language translation that uses deep learning models to deliver more accurate and natural sounding translation that traditional statistical and rule-based algorithms; 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

SageMaker Feature Store

a purpose-built repository where you can store and access features so it's much easier to name, organize and reuse them across teams; 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; keeps track of the metadata of stored features (name, version number, etc.) so that you can query the features for the right attributes in batches or in real time using Athena; also keeps features updated as new data is generated during inference so that new features are always available in the repository to use during training and inference

Personalize

an ML service that makes it easy for developers to create individualized recommendations for customers using their applications; ML is often used to improve customer engagement by powering personalized product and content recommendations, tailored search results, and targeting marketing campaigns; 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 by Amazon; provide an activity stream from your application - page views, signups, purchases, etc. - and an inventory of the items you want to recommend (articles, products, videos, music); you can also choose to provide additional demographic information from your users such as age or geographic location; the service processes and examines the data, identifies what is meaningful and selects the right algorithms, training and optimizing a personalization model that is customized for your data; offers optimized recommenders for retail and media and entertainment that make it faster and easier to deliver high-performing personalized user experiences; also offers intelligent user segmentation so you can run more effective prospecting campaigns through your marketing channels; you can automatically segment your users based on their interest in different product categories, brands and more; all data is kept private and secure, only to be used for your customized recommendations; serve personalized predictions via a simple API call from inside the VPC that the service maintains; pay only for what you use, no minimum fees or upfront commitments

Lookout for Vision

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

DevOps Guru

an ML-powered service that makes it easy to improve an applications operational performance and availability; detects behaviors that deviate from normal operating patterns so you can identify operational issues before they impact your customers; uses ML models informed by years of experience to identify anomalous behavior (increased latency, error rates, resource constraints etc.) and surface critical issues that could cause outages or disruptions; when a critical issue is identified, the service 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, recommendations for a fix are also provided; automatically ingests operational data from your AWS applications and provides a single dashboard to visualize issues; get started by enabling DevOps Guru for all resources in your AWS account, resources in your CloudFormation stacks or resources grouped together by tags with no manual setup or ML expertise required

Transcribe

an automatic speech recognition (ASR) service that makes it easy for customers to automatically convert speech to text; transcribe audio files stored in common formats (like WAV, MP3), with time stamps for every word so that you can easily locate the audio in the original source by searching for the text; live audio streams can also be sent to receive real-time transcripts; designed to handle a wide range of speech and acoustic characteristics, including variations in volume, pitch and speaking rate; quality and content of the audio signal (background noise, overlapping speakers, accented speech, language switching, etc.) may affect the accuracy of the transcription; use cases include transcribing customer service calls, subtitle generation, and content analysis; easily get started by submitting a job using the console or AWS CLI or SDK

Monitron

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; sensors and necessary infrastructure for data connectivity, storage, analytics and alerting are foundational to predictive maintenance, but to make it work skilled technicians and data scientists are traditionally required to build a solution from scratch; Monitron includes sensors to capture vibration and temperature data from equipment, a gateway device to securely transfer data to AWS, the service that analyzes the data for abnormal patterns using ML and a companion mobile app to set up the devices and receive reports on operating behavior and alerts to potential failures; start monitoring equipment health in minutes without any development work or ML experience required and enable predictive maintenance with the same technology used by Amazon

Kendra

an intelligent search service powered by ML; search through unstructured data to gain insights when needed; fully managed service with no servers to provision and no models to build, train, deploy

PyTorch

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), developers can quickly and easily deploy models to production; also provides dynamic computation graphs and libraries for distributed training, which are tuned for high performance on AWS; get started using SageMaker or manage the infrastructure yourself using the AMIs or DLCs which come built with the latest version of PyTorch

Lookout for Equipment

analyzes the data from the sensors on your equipment (pressure in a generator, flow rate of a compressor, etc.) to automatically train an ML model based on only your data for your equipment - no ML expertise required; 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; detect equipment abnormalities with speed and precision, quickly diagnose issues and take action to reduce expensive downtime and reduce false alerts

SageMaker Autopilot

automatically builds, trains, and tunes the best ML models based on your data, while allowing you to maintain full control and visibility; simply provide a tabular dataset and select the target column to predict, which can be a number (regression) or a category (classification); Autopilot will automatically explore different solutions to find the best model; directly deploy the model to production with just one click or iterate on the recommended solutions with SageMaker studio to further improve model quality

SageMaker

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

Hugging Face

deploy and fine-tune pre-trained models from Hugging Face, an open-source provider of NLP models known as Transformers, reducing the time it takes to set up and use these NLP models from weeks to minutes; AWS collaborated with Hugging Face to create Hugging Face 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 SageMaker

Transcribe Medical

derived from Transcribe, uses advanced ML models to accurately transcribe medical speech into text; can generate text transcripts that support a variety of use cases (clinical documentation workflow, drug safety monitoring, subtitling for telemedicine and contact center analytics in the healthcare and life sciences domain

CodeWhisperer

designed to improve developer productivity, provides ML-powered code recommendations to accelerate development of C#, Java, JavaScript, Python and TypeScript applications; integrated with multiple IDEs including JetBrains (IDEA, PyCharm, WebStorm and Rider), Visual Studio Code, AWS Cloud9, and the AWS Lambda console and helps developers write code faster by generating entire functions and logical blocks of code (often more than 10-15 lines)

SageMaker Edge

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

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 writing any code

DeepLens

helps put deep learning in the hands of developers with a fully programmable video camera, tutorials, code and pre-trained models designed to expand deep learning skills

SageMaker JumpStart

helps you quickly and easily get started with ML; provides a set of solutions for the most common use cases that can be deployed readily with just a few clicks; solutions are fully customizable and showcase the use of AWS CloudFormation templates and reference architectures so you can accelerate your ML journey; 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

SageMaker geospatial capabilities

make it easier for data scientists and ML engineers to build, train, deploy models faster using geospatial data; access data (open-source and third-party), processing, and visualization tools to make it more efficient to prepare geospatial data for ML; 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 collaborate across teams on insights and results

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; identify objects, people, text, scenes and activities in images and videos as well as detect any inappropriate content; provides highly accurate facial analysis and 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; identify the objects and scenes in images that are specific to your business needs (e.g. build a model to classify specific machine parts on your assembly line or to detect unhealthy plants); model development is handled by the service so no ML experience is required; simply supply images of objects or scenes you want to identify

SageMaker Model Deployment

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

TensorFlow

one of many deep learning frameworks available to researchers and developers to enhance their applications with machine learning; develop and serve your own models across computer vision, NLP, speech translation and more; get started using SageMaker or AMIs/DLCs

Deep Learning AMIs

provide ML practitioners and researchers with the infrastructure and tools to accelerate deep learning in the cloud at any scale; quickly launch 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 learn new skills and techniques; whether you need GPU or CPU instances, there is no additional charge for the Deep Learning AMIs - only pay for the AWS resources needed to store and run applications

SageMaker Clarify

provides ML developers with greater visibility into their training data and models so they can identify and limit bias and explain predictions; detects potential bias during data preparation, after model training, and in your deployed model by examining specified attributes; 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 can then be corrected

SageMaker Model Building

provides all the tools and libraries needed to build ML models (the process of iteratively trying different algorithms and evaluating their accuracy to find the best one for your use case); pick different algorithms, including over 15 that are built-in and optimized for SageMaker and over 150 pre-built models from popular model zoos available with a few clicks; also offers a variety of model building tools, including SageMaker Studio Notebooks and RStudio, where you can run ML models on a small scale to see results and view reports on their performance for high-quality prototypes

SageMaker Data Labeling

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 ML models

SageMaker Model Training

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

SageMaker Data Wrangler

reduces the time it takes to aggregate and prepare data for ML from weeks to minutes; 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

SageMaker Pipelines

the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for ML; create, automate and manage end-to-end ML workflows at scale

DeepComposer

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; consists of a USB keyboard that connects to the developers computer and the service, accessed through the console; includes tutorials, sample code, training data that can be used to start building generative models

Comprehend

uses ML and NLP to help you uncover the insights and relationships in your unstructured data; 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; also use AutoML capabilities in Comprehend to build a custom set of entities or text classification models that are tailored uniquely to your needs; for extracting complex medical information from unstructured text, use Comprehend Medical - this 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; also identifies the relationship among the extracted medication and test, treatment and procedure information for easier analysis (e.g. identifies a particular dosage, strength and frequency related to a particular medication)

Lookout for Metrics

uses ML to automatically detect and diagnose anomalies in business and operational data (a sudden dip in sales revenue or customer acquisition rates); connect to popular data stores such as S3, RedShift, RDS, third-party SaaS applications (Salesforce, Servicenow, Zendesk, Marketo) and start monitoring metrics that are important to your business; automatically inspects and prepares the data from these sources to detect anomalies with greater speed and accuracy than traditional methods; provide feedback on detected anomalies to tune the results and improve accuracy over time; 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; ranks anomalies in order of severity so that you can prioritize your attention accordingly