Machine Learning
Amazon SageMaker
Amazon SageMaker is a fully-managed platform
that enables developers and data scientists to quickly
and easily build, train, and deploy machine learning models at any scale. Amazon
SageMaker removes all the barriers that typically slow down developers who want
to use machine learning.
Machine learning often feels a lot harder than
it should be to most developers because the process to build and train models,
and then deploy them into production is too complicated and too slow. First,
you need to collect and prepare your training data to discover which elements
of your data set are important. Then, you need to select which algorithm and
framework you’ll use. After deciding on your approach, you need to teach the
model how to make predictions by training, which requires a lot of compute.
Then, you need to tune the model so it delivers the best possible predictions,
which is often a tedious and manual effort. After you’ve developed a fully
trained model, you need to integrate the model with your application and deploy
this application on infrastructure that will scale. All of this takes a lot of
specialized expertise, access to large amounts of compute and storage, and a
lot of time to experiment and optimize every part of the process. In the end,
it’s not a surprise that the whole thing feels out of reach for most
developers.
Amazon SageMaker removes the complexity that
holds back developer success with each of these steps. Amazon SageMaker
includes modules that can be used together or independently to build, train,
and deploy your machine learning models.
Amazon SageMaker Ground Truth
Amazon SageMaker Ground Truth helps you build
highly accurate training datasets for machine learning quickly. SageMaker Ground
Truth offers easy access to public and private human labelers and provides them
with built-in workflows and interfaces for common labeling tasks. Additionally,
SageMaker Ground Truth can lower your labeling costs by up to 70% using
automatic labeling, which works by training Ground Truth from data labeled by
humans so that the service learns to label data independently.
Successful machine learning models are built
on the shoulders of large volumes of high-quality training data. But, the
process to create the training data necessary to build these models is often
expensive, complicated, and time-consuming. The majority of models created
today require a human to manually label data in a way that allows the model to
learn how to make correct decisions. For example, building a computer vision
system that is reliable enough to identify objects – such as traffic lights,
stop signs, and pedestrians – requires thousands of hours of video recordings
that consist of hundreds of millions of video frames. Each one of these frames
needs all of the important elements like the road, other cars, and signage to
be labeled by a human before any work can begin on the model you want to
develop.
Amazon SageMaker Ground Truth significantly
reduces the time and effort required to create datasets for training to reduce
costs. These savings are achieved by using machine learning to automatically
label data. The model is able to get progressively better over time by
continuously learning from labels created by human labelers.
Where the labeling model has high confidence
in its results based on what it has learned so far, it will automatically apply
labels to the raw data. Where the labeling model has lower confidence in its
results, it will pass the data to humans to do the labeling. The
human-generated labels are provided back to the labeling model for it to learn
from and improve. Over time, SageMaker Ground Truth can label more and more
data automatically and substantially speed up the creation of training
datasets.
Amazon Comprehend
Amazon Comprehend is a natural language
processing (NLP) service that uses machine learning to find insights and relationships in text. No machine
learning experience required.
There is a treasure trove of potential sitting
in your unstructured data. Customer emails, support tickets, product reviews,
social media, even advertising copy represents insights into customer sentiment
that can be put to work for your business. The question is how to get at it? As
it turns out, Machine learning is particularly good at accurately identifying
specific items of interest inside vast swathes of text (such as finding company
names in analyst reports), and can learn the sentiment hidden inside language
(identifying negative reviews, or positive customer interactions with customer
service agents), at almost limitless scale.
Amazon Comprehend uses machine learning to
help you uncover the insights and relationships in your unstructured data. The
service identifies the language of the text; extracts key phrases, places, people,
brands, or events; understands how positive or negative the text is; analyzes
text using tokenization and parts of speech; and automatically organizes a
collection of text files by topic. You can also use AutoML capabilities in
Amazon Comprehend to build a custom set of entities or text classification
models that are tailored uniquely to your organization’s needs.
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 Lex
Amazon Lex is a service for building
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. With Amazon Lex, the same deep learning
technologies that power Amazon Alexa are now available to any developer,
enabling you to quickly and easily build sophisticated, natural language,
conversational bots (“chatbots”).
Speech recognition and natural language
understanding are some of the most challenging problems to solve in computer
science, requiring sophisticated deep learning algorithms to be trained on
massive amounts of data and infrastructure. Amazon Lex democratizes these deep
learning technologies by putting the power of Alexa within reach of all
developers. Harnessing these technologies, Amazon Lex enables you to define
entirely new categories of products made possible through conversational
interfaces.
Amazon Polly
Amazon Polly is a service that turns text into lifelike speech. Polly lets you
create applications that talk, enabling you to build entirely new categories of
speech-enabled products. Polly is an Amazon artificial intelligence (AI)
service that uses advanced deep learning technologies to synthesize speech that
sounds like a human voice. Polly includes 47 lifelike voices spread across 24
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 Polly’s speech audio to replay offline or redistribute. And Polly is
easy to use. You simply send the text you want converted into speech to the
Polly API, and 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. With Polly, you only pay for the number of characters you
convert to speech, and you can save and replay Polly’s generated speech.
Polly’s 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 is a service that makes it
easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images.
You can also search and compare faces. The Amazon Rekognition API enables you
to quickly add sophisticated deep-learning-based visual search and image
classification to your applications.
Amazon Rekognition is based on the same
proven, highly scalable, deep learning technology developed by Amazon’s
computer vision scientists to analyze billions of images daily for Prime
Photos. Amazon Rekognition uses deep neural network models to detect and label
thousands of objects and scenes in your images, and we are continually adding
new labels and facial recognition features to the service.
The Amazon Rekognition API lets you easily
build powerful visual search and discovery into your applications. With Amazon
Rekognition, you only pay for the images you analyze and the face metadata you
store. There are no minimum fees, and there are no upfront commitments.
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 international users, and to easily translate large volumes of text
efficiently.
Amazon Transcribe
Amazon Transcribe is an automatic
speech recognition (ASR) service that
makes it easy for developers to add speech-to-text
capability to their applications. Using the Amazon Transcribe API, you
can analyze audio files stored in Amazon S3 and have the service return a text
file of the transcribed speech. You can also send a live audio stream to Amazon
Transcribe and receive a stream of transcripts in real time.
Amazon Transcribe can be used for lots of
common applications, including the transcription of customer service calls and
generating subtitles on audio and video content. 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. Amazon Transcribe is continually learning and improving
to keep pace with the evolution of language.
Amazon Elastic Inference
Amazon Elastic Inference allows you to attach
low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances
to reduce the cost of running deep learning inference by up to 75%. Amazon
Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more
frameworks coming soon.
In most deep learning applications, making
predictions using a trained model—a process called inference—can drive as much
as 90% of the compute costs of the application due to two factors. First,
standalone GPU instances are designed for model training and are typically
oversized for inference. While training jobs batch process hundreds of data
samples in parallel, most inference happens on a single input in real time that
consumes only a small amount of GPU compute. Even at peak load, a GPU’s compute
capacity may not be fully utilized, which is wasteful and costly. Second,
different models need different amounts of GPU, CPU, and memory resources.
Selecting a GPU instance type that is big enough to satisfy the requirements of
the least used resource often results in under-utilization of the other
resources and high costs.
Amazon Elastic Inference solves these problems
by allowing you to attach just the right amount of GPU-powered inference
acceleration to any EC2 or SageMaker instance type with no code changes. With
Amazon Elastic Inference, you can now choose the instance type that is best
suited to the overall CPU and memory needs of your application, and then
separately configure the amount of inference acceleration that you need to use
resources efficiently and to reduce the cost of running inference.
Amazon Forecast
Amazon Forecast is a fully managed service
that uses machine learning to deliver highly accurate
forecasts.
Companies today use everything from simple
spreadsheets to complex financial planning software to attempt to accurately
forecast future business outcomes such as product demand, resource needs, or financial
performance. These tools build forecasts by looking at a historical series of
data, which is called time series data. For example, such tools may try to
predict the future sales of a raincoat by looking only at its previous sales
data with the underlying assumption that the future is determined by the past.
This approach can struggle to produce accurate forecasts for large sets of data
that have irregular trends. Also, it fails to easily combine data series that
change over time (such as price, discounts, web traffic, and number of
employees) with relevant independent variables like product features and store
locations.
Based on the same technology used at
Amazon.com, Amazon Forecast uses machine learning to combine time series data
with additional variables to build forecasts. Amazon Forecast requires no
machine learning experience to get started. You only need to provide historical
data, plus any additional data that you believe may impact your forecasts. For
example, the demand for a particular color of a shirt may change with the
seasons and store location. This complex relationship is hard to determine on
its own, but machine learning is ideally suited to recognize it. Once you
provide your data, Amazon Forecast will automatically examine it, identify what
is meaningful, and produce a forecasting model capable of making predictions
that are up to 50% more accurate than looking at time series data alone.
Amazon Forecast is a fully managed service, so
there are no servers to provision, and no machine learning models to build,
train, or deploy. You pay only for what you use, and there are no minimum fees
and no upfront commitments.
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.
Many companies today extract data from
documents and forms through manual data entry that’s slow and expensive or
through simple optical character recognition (OCR) software that is difficult
to customize. Rules and workflows for each document and form often need to be
hard-coded and updated with each change to the form or when dealing with
multiple forms. If the form deviates from the rules, the output is often
scrambled and unusable.
Amazon Textract overcomes these challenges by
using machine learning to instantly “read” virtually any type of document to
accurately extract text and data without the need for any manual effort or
custom code. With Textract you can quickly automate document workflows,
enabling you to process millions of document pages in hours. Once the
information is captured, you can take action on it within your business
applications to initiate next steps for a loan application or medical claims
processing. Additionally, you can create smart search indexes, build automated
approval workflows, and better maintain compliance with document archival rules
by flagging data that may require redaction.
Amazon Personalize
Amazon Personalize is a machine learning
service that makes it easy for developers to create individualized
recommendations for customers using their applications.
Machine learning is being increasingly used to
improve customer engagement by powering personalized product and content
recommendations, tailored search results, and targeted marketing promotions.
However, developing the machine-learning capabilities necessary to produce
these sophisticated recommendation systems has been beyond the reach of most
organizations today due to the complexity of developing machine learning
functionality. Amazon Personalize allows developers with no prior machine
learning experience to easily build sophisticated personalization capabilities
into their applications, using machine learning technology perfected from years
of use on Amazon.com.
With Amazon Personalize, you provide an
activity stream from your application – page views, signups, purchases, and so
forth – as well as an inventory of the items you want to recommend, such as
articles, products, videos, or music. You can also choose to provide Amazon
Personalize with additional demographic information from your users such as
age, or geographic location. Amazon Personalize will process and examine the
data, identify what is meaningful, select the right algorithms, and train and
optimize a personalization model that is customized for your data.
All data analyzed by Amazon Personalize is
kept private and secure, and only used for your customized recommendations. You
can start serving 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 machine learning personalization team at your disposal, 24 hours a
day.
Amazon Deep Learning AMIs
The Amazon Deep Learning AMIs provide machine
learning practitioners and researchers with the infrastructure and tools to accelerate
deep learning in the cloud, at any scale. You can quickly launch Amazon EC2
instances pre-installed with popular deep learning frameworks such as Apache
MXNet and Gluon, TensorFlow, Microsoft Cognitive Toolkit, Caffe, Caffe2,
Theano, Torch, PyTorch, Chainer, and Keras to train sophisticated, custom AI
models, experiment with new algorithms, or to learn new skills and techniques.
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 machine learning (ML) technique which takes a very
different approach to training models than other machine learning methods. Its
super power 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.
Apache MXNet on AWS
Apache MXNet on AWS is a fast and scalable
training and inference framework with an easy-to-use, concise API for machine
learning.
MXNet includes the Gluon interface that allows
developers of all skill levels to get started with deep learning on the cloud,
on edge devices, and on mobile apps. In just a few lines of Gluon code, you can
build linear regression, convolutional networks and recurrent LSTMs for object
detection, speech recognition, recommendation, and personalization.
You can get started with MxNet on AWS with a
fully-managed experience using Amazon SageMaker, a platform to build, train,
and deploy machine learning models at scale. Or, you can use the AWS Deep
Learning AMIs to build custom environments and workflows with MxNet as well as
other frameworks including TensorFlow, PyTorch, Chainer, Keras, Caffe, Caffe2,
and Microsoft Cognitive Toolkit.
TensorFlow on AWS
TensorFlow™ enables developers to quickly and
easily get started with deep learning in the cloud. The framework has broad
support in the industry and has become a popular choice for deep learning
research and application development, particularly in areas such as computer
vision, natural language understanding and speech translation.
You can get started on AWS with a
fully-managed TensorFlow experience with Amazon SageMaker, a platform to build,
train, and deploy machine learning models at scale. Or, you can use the AWS
Deep Learning AMIs to build custom environments and workflows with TensorFlow
and other popular frameworks including Apache MXNet, PyTorch, Caffe, Caffe2,
Chainer, Gluon, Keras, and Microsoft Cognitive Toolkit.
AWS Inferentia
AWS Inferentia is a machine learning inference
chip designed to deliver high performance at low cost. AWS Inferentia will
support the TensorFlow, Apache MXNet, and PyTorch deep learning frameworks, as
well as models that use the ONNX format.
Making predictions using a trained machine
learning model–a process called inference–can drive as much as 90% of the
compute costs of the application. Using Amazon Elastic Inference, developers
can reduce inference costs by up to 75% by attaching GPU-powered inference
acceleration to Amazon EC2 and Amazon SageMaker instances. However, some
inference workloads require an entire GPU or have extremely low latency
requirements. Solving this challenge at low cost requires a dedicated inference
chip.
AWS Inferentia provides high throughput, low
latency inference performance at an extremely low cost. Each chip provides
hundreds of TOPS (tera operations per second) of inference throughput to allow
complex models to make fast predictions. For even more performance, multiple
AWS Inferentia chips can be used together to drive thousands of TOPS of
throughput. AWS Inferentia will be available for use with Amazon SageMaker,
Amazon EC2, and Amazon Elastic Inference.