Machine Learning (ML) and Artificial Intelligence (AI)
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 CodeWhisperer
Designed to improve developer productivity, Amazon CodeWhisperer provides ML–powered code
recommendations to accelerate development of C#, Java, JavaScript, Python, and TypeScript
applications. The service integrates with multiple integrated development environments (IDEs),
including JetBrains (IntelliJ 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 consisting of more than 10–15 lines of code.
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 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 executable 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 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.
AWS Deep Learning AMIs
The AWS Deep Learning AMI 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 AMIs 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 AMIs 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 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 DeepLens
AWS DeepLens helps put deep learning in the hands of
developers, literally, with a fully programmable video camera, tutorials, code, and pre-trained
models designed to expand deep learning skills.
AWS DeepRacer
AWS DeepRacer is a
1/18th 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.
