Amazon SageMaker Role Manager makes it easier
for administrators to control access and define permissions for
improved machine learning governance
Amazon SageMaker Model Cards make it easier to
document and review model information throughout the machine
learning lifecycle
Amazon SageMaker Model Dashboard provides a
central interface to track models, monitor performance, and review
historical behavior
New data preparation capability in Amazon
SageMaker Studio Notebooks helps customers visually inspect and
address data-quality issues in a few clicks
Data science teams can now collaborate in real
time within Amazon SageMaker Studio Notebook
Customers can now automatically convert
notebook code into production-ready jobs
Automated model validation enables customers to
test new models using real-time inference requests
Support for geospatial data enables customers
to more easily develop machine learning models for climate science,
urban planning, disaster response, retail planning, precision
agriculture, and more
At AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com, Inc. company (NASDAQ: AMZN), today announced eight new
capabilities for Amazon SageMaker, its end-to-end machine learning
(ML) service. Developers, data scientists, and business analysts
use Amazon SageMaker to build, train, and deploy ML models quickly
and easily using its fully managed infrastructure, tools, and
workflows. As customers continue to innovate using ML, they are
creating more models than ever before and need advanced
capabilities to efficiently manage model development, usage, and
performance. Today’s announcement includes new Amazon SageMaker
governance capabilities that provide visibility into model
performance throughout the ML lifecycle. New Amazon SageMaker
Studio Notebook capabilities provide an enhanced notebook
experience that enables customers to inspect and address
data-quality issues in just a few clicks, facilitate real-time
collaboration across data science teams, and accelerate the process
of going from experimentation to production by converting notebook
code into automated jobs. Finally, new capabilities within Amazon
SageMaker automate model validation and make it easier to work with
geospatial data. To get started with Amazon SageMaker, visit
aws.amazon.com/sagemaker.
“Today, tens of thousands of customers of all sizes and across
industries rely on Amazon SageMaker. AWS customers are building
millions of models, training models with billions of parameters,
and generating trillions of predictions every month. Many customers
are using ML at a scale that was unheard of just a few years ago,”
said Bratin Saha, vice president of Artificial Intelligence and
Machine Learning at AWS. “The new Amazon SageMaker capabilities
announced today make it even easier for teams to expedite the
end-to-end development and deployment of ML models. From
purpose-built governance tools to a next-generation notebook
experience and streamlined model testing to enhanced support for
geospatial data, we are building on Amazon SageMaker’s success to
help customers take advantage of ML at scale.”
The cloud enabled access to ML for more users, but until a few
years ago, the process of building, training, and deploying models
remained painstaking and tedious, requiring continuous iteration by
small teams of data scientists for weeks or months before a model
was production-ready. Amazon SageMaker launched five years ago to
address these challenges, and since then AWS has added more than
250 new features and capabilities to make it easier for customers
to use ML across their businesses. Today, some customers employ
hundreds of practitioners who use Amazon SageMaker to make
predictions that help solve the toughest challenges around
improving customer experience, optimizing business processes, and
accelerating the development of new products and services. As ML
adoption has increased, so have the types of data that customers
want to use, as well as the levels of governance, automation, and
quality assurance that customers need to support the responsible
use of ML. Today's announcement builds on Amazon SageMaker's
history of innovation in supporting practitioners of all skill
levels, worldwide.
New ML governance capabilities in Amazon SageMaker
Amazon SageMaker offers new capabilities that help customers
more easily scale governance across the ML model lifecycle. As the
number of models and users within an organization increases, it
becomes harder to set least-privilege access controls and establish
governance processes to document model information (e.g., input
data sets, training environment information, model-use description,
and risk rating). Once models are deployed, customers also need to
monitor for bias and feature drift to ensure they perform as
expected.
- Amazon SageMaker Role Manager makes it easier to control
access and permissions: Appropriate user-access controls are a
cornerstone of governance and support data privacy, prevent
information leaks, and ensure practitioners can access the tools
they need to do their jobs. Implementing these controls becomes
increasingly complex as data science teams swell to dozens or even
hundreds of people. ML administrators—individuals who create and
monitor an organization’s ML systems—must balance the push to
streamline development while controlling access to tasks,
resources, and data within ML workflows. Today, administrators
create spreadsheets or use ad hoc lists to navigate access policies
needed for dozens of different activities (e.g., data prep and
training) and roles (e.g., ML engineer and data scientist).
Maintaining these tools is manual, and it can take weeks to
determine the specific tasks new users will need to do their jobs
effectively. Amazon SageMaker Role Manager makes it easier for
administrators to control access and define permissions for users.
Administrators can select and edit prebuilt templates based on
various user roles and responsibilities. The tool then
automatically creates the access policies with necessary
permissions within minutes, reducing the time and effort to onboard
and manage users over time.
- Amazon SageMaker Model Cards simplify model information
gathering: Today, most practitioners rely on disparate tools
(e.g., email, spreadsheets, and text files) to document the
business requirements, key decisions, and observations during model
development and evaluation. Practitioners need this information to
support approval workflows, registration, audits, customer
inquiries, and monitoring, but it can take months to gather these
details for each model. Some practitioners try to solve this by
building complex recordkeeping systems, which is manual, time
consuming, and error-prone. Amazon SageMaker Model Cards provide a
single location to store model information in the AWS console,
streamlining documentation throughout a model’s lifecycle. The new
capability auto-populates training details like input datasets,
training environment, and training results directly into Amazon
SageMaker Model Cards. Practitioners can also include additional
information using a self-guided questionnaire to document model
information (e.g., performance goals, risk rating), training and
evaluation results (e.g., bias or accuracy measurements), and
observations for future reference to further improve governance and
support the responsible use of ML.
- Amazon SageMaker Model Dashboard provides a central
interface to track ML models: Once a model has been deployed to
production, practitioners want to track their model over time to
understand how it performs and to identify potential issues. This
task is normally done on an individual basis for each model, but as
an organization starts to deploy thousands of models, this becomes
increasingly complex and requires more time and resources. Amazon
SageMaker Model Dashboard provides a comprehensive overview of
deployed models and endpoints, enabling practitioners to track
resources and model behavior in one place. From the dashboard,
customers can also use built-in integrations with Amazon SageMaker
Model Monitor (AWS’s model and data drift monitoring capability)
and Amazon SageMaker Clarify (AWS’s ML bias-detection capability).
This end-to-end visibility into model behavior and performance
provides the necessary information to streamline ML governance
processes and quickly troubleshoot model issues.
To learn more about Amazon SageMaker governance capabilities,
visit aws.amazon.com/sagemaker/ml-governance.
Next-generation Notebooks
Amazon SageMaker Studio Notebook gives practitioners a fully
managed notebook experience, from data exploration to deployment.
As teams grow in size and complexity, dozens of practitioners may
need to collaboratively develop models using notebooks. AWS
continues to offer the best notebook experience for users with the
launch of three new features that help customers coordinate and
automate their notebook code.
- Simplified data preparation: Practitioners want to
explore datasets directly in notebooks to spot and correct
potential data-quality issues (e.g., missing information, extreme
values, skewed datasets, and biases) as they prepare data for
training. Practitioners can spend months writing boilerplate code
to visualize and examine different parts of their dataset to
identify and fix problems. Amazon SageMaker Studio Notebook now
offers a built-in data preparation capability that allows
practitioners to visually review data characteristics and remediate
data-quality problems in just a few clicks—all directly in their
notebook environment. When users display a data frame (i.e., a
tabular representation of data) in their notebook, Amazon SageMaker
Studio Notebook automatically generates charts to help users
identify data-quality issues and suggests data transformations to
help fix common problems. Once the practitioner selects a data
transformation, Amazon SageMaker Studio Notebook generates the
corresponding code within the notebook so it can be repeatedly
applied every time the notebook is run.
- Accelerate collaboration across data science teams:
After data has been prepared, practitioners are ready to start
developing a model—an iterative process that may require teammates
to collaborate within a single notebook. Today, teams must exchange
notebooks and other assets (e.g., models and datasets) over email
or chat applications to work on a notebook together in real time,
leading to communication fatigue, delayed feedback loops, and
version-control issues. Amazon SageMaker now gives teams a
workspace where they can read, edit, and run notebooks together in
real time to streamline collaboration and communication. Teammates
can review notebook results together to immediately understand how
a model performs, without passing information back and forth. With
built-in support for services like BitBucket and AWS CodeCommit,
teams can easily manage different notebook versions and compare
changes over time. Affiliated resources, like experiments and ML
models, are also automatically saved to help teams stay
organized.
- Automatic conversion of notebook code to production-ready
jobs: When practitioners want to move a finished ML model into
production, they usually copy snippets of code from the notebook
into a script, package the script with all its dependencies into a
container, and schedule the container to run. To run this job
repeatedly on a schedule, they must set up, configure, and manage a
continuous integration and continuous delivery (CI/CD) pipeline to
automate their deployments. It can take weeks to get all the
necessary infrastructure set up, which takes time away from core ML
development activities. Amazon SageMaker Studio Notebook now allows
practitioners to select a notebook and automate it as a job that
can run in a production environment. Once a notebook is selected,
Amazon SageMaker Studio Notebook takes a snapshot of the entire
notebook, packages its dependencies in a container, builds the
infrastructure, runs the notebook as an automated job on a schedule
set by the practitioner, and deprovisions the infrastructure upon
job completion, reducing the time it takes to move a notebook to
production from weeks to hours.
To begin using the next generation of Amazon SageMaker Studio
Notebooks and these new capabilities, visit
aws.amazon.com/sagemaker/notebooks.
Automated validation of new models using real-time inference
requests
Before deploying to production, practitioners test and validate
every model to check performance and identify errors that could
negatively impact the business. Typically, they use historical
inference request data to test the performance of a new model, but
this data sometimes fails to account for current, real-world
inference requests. For example, historical data for an ML model to
plan the fastest route might fail to account for an accident or a
sudden road closure that significantly alters the flow of traffic.
To address this issue, practitioners route a copy of the inference
requests going to a production model to the new model they want to
test. It can take weeks to build this testing infrastructure,
mirror inference requests, and compare how models perform across
key metrics (e.g., latency and throughput). While this provides
practitioners with greater confidence in how the model will
perform, the cost and complexity of implementing these solutions
for hundreds or thousands of models makes it unscalable.
Amazon SageMaker Inference now provides a capability to make it
easier for practitioners to compare the performance of new models
against production models, using the same real-world inference
request data in real time. Now, they can easily scale their testing
to thousands of new models simultaneously, without building their
own testing infrastructure. To start, a customer selects the
production model they want to test against, and Amazon SageMaker
Inference deploys the new model to a hosting environment with the
exact same conditions. Amazon SageMaker routes a copy of the
inference requests received by the production model to the new
model and creates a dashboard to display performance differences
across key metrics, so customers can see how each model differs in
real time. Once the customer validates the new model’s performance
and is confident it is free of potential errors, they can safely
deploy it. To learn more about Amazon SageMaker Inference, visit
aws.amazon.com/sagemaker/shadow-testing.
New geospatial capabilities in Amazon SageMaker make it
easier for customers to make predictions using satellite and
location data
Today, most data captured has geospatial information (e.g.,
location coordinates, weather maps, and traffic data). However,
only a small amount of it is used for ML purposes because
geospatial datasets are difficult to work with and can often be
petabytes in size, spanning entire cities or hundreds of acres of
land. To start building a geospatial model, customers typically
augment their proprietary data by procuring third-party data
sources like satellite imagery or map data. Practitioners need to
combine this data, prepare it for training, and then write code to
divide datasets into manageable subsets due to the massive size of
geospatial data. Once customers are ready to deploy their trained
models, they must write more code to recombine multiple datasets to
correlate the data and ML model predictions. To extract predictions
from a finished model, practitioners then need to spend days using
open source visualization tools to render on a map. The entire
process from data enrichment to visualization can take months,
which makes it hard for customers to take advantage of geospatial
data and generate timely ML predictions.
Amazon SageMaker now accelerates and simplifies generating
geospatial ML predictions by enabling customers to enrich their
datasets, train geospatial models, and visualize the results in
hours instead of months. With just a few clicks or using an API,
customers can use Amazon SageMaker to access a range of geospatial
data sources from AWS (e.g., Amazon Location Service), open-source
datasets (e.g., Amazon Open Data), or their own proprietary data
including from third-party providers (like Planet Labs). Once a
practitioner has selected the datasets they want to use, they can
take advantage of built-in operators to combine these datasets with
their own proprietary data. To speed up model development, Amazon
SageMaker provides access to pre-trained deep-learning models for
use cases such as increasing crop yields with precision
agriculture, monitoring areas after natural disasters, and
improving urban planning. After training, the built-in
visualization tool displays data on a map to uncover new
predictions. To learn more about Amazon SageMaker’s new geospatial
capability, visit aws.amazon.com/sagemaker/geospatial.
Capitec Bank is South Africa's largest digital bank with over 10
million digital clients. “At Capitec, we have a wide range of data
scientists across our product lines who build differing ML
solutions,” said Dean Matter, ML engineer at Capitec Bank. “Our ML
engineers manage a centralized modeling platform built on Amazon
SageMaker to empower the development and deployment of all of these
ML solutions. Without any built-in tools, tracking modelling
efforts tends toward disjointed documentation and a lack of model
visibility. With Amazon SageMaker Model Cards, we can track plenty
of model metadata in a unified environment, and Amazon SageMaker
Model Dashboard provides visibility into the performance of each
model. In addition, Amazon SageMaker Role Manager simplifies access
management for data scientists in our different product lines. Each
of these contribute toward our model governance being sufficient to
warrant the trust that our clients place in us as a financial
services provider.”
EarthOptics is a soil-data-measurement and mapping company that
leverages proprietary sensor technology and data analytics to
precisely measure the health and structure of soil. “We wanted to
use ML to help customers increase agricultural yields with
cost-effective soil maps,” said Lars Dyrud, CEO of EarthOptics.
“Amazon SageMaker’s geospatial ML capabilities allowed us to
rapidly prototype algorithms with multiple data sources and reduce
the amount of time between research and production API deployment
to just a month. Thanks to Amazon SageMaker, we now have geospatial
solutions for soil carbon sequestration deployed for farms and
ranches across the U.S.”
HERE Technologies is a leading location-data and technology
platform that helps customers create custom maps and location
experiences built on highly precise location data. “Our customers
need real-time context as they make business decisions leveraging
insights from spatial patterns and trends,” said Giovanni
Lanfranchi, chief product and technology officer for HERE
Technologies. “We rely on ML to automate the ingestion of
location-based data from varied sources to enrich it with context
and accelerate analysis. Amazon SageMaker’s new testing
capabilities allowed us to more rigorously and proactively test ML
models in production and avoid adverse customer impact and any
potential outages because of an error in deployed models. This is
critical, since our customers rely on us to provide timely insights
based on real-time location data that changes every minute.”
Intuit is the global financial technology platform that powers
prosperity for more than 100 million customers worldwide with
TurboTax, Credit Karma, QuickBooks, and Mailchimp. “We’re
unleashing the power of data to transform the world of consumer,
self-employed, and small business finances on our platform,” said
Brett Hollman, director of Engineering and Product Development at
Intuit. “To further improve team efficiencies for getting AI-driven
products to market with speed, we've worked closely with AWS in
designing the new team-based collaboration capabilities of
SageMaker Studio Notebooks. We’re excited to streamline
communication and collaboration to enable our teams to scale ML
development with Amazon SageMaker Studio.”
About Amazon Web Services
For over 15 years, Amazon Web Services has been the world’s most
comprehensive and broadly adopted cloud offering. AWS has been
continually expanding its services to support virtually any cloud
workload, and it now has more than 200 fully featured services for
compute, storage, databases, networking, analytics, machine
learning and artificial intelligence (AI), Internet of Things
(IoT), mobile, security, hybrid, virtual and augmented reality (VR
and AR), media, and application development, deployment, and
management from 96 Availability Zones within 30 geographic regions,
with announced plans for 15 more Availability Zones and five more
AWS Regions in Australia, Canada, Israel, New Zealand, and
Thailand. Millions of customers—including the fastest-growing
startups, largest enterprises, and leading government
agencies—trust AWS to power their infrastructure, become more
agile, and lower costs. To learn more about AWS, visit
aws.amazon.com.
About Amazon
Amazon is guided by four principles: customer obsession rather
than competitor focus, passion for invention, commitment to
operational excellence, and long-term thinking. Amazon strives to
be Earth’s Most Customer-Centric Company, Earth’s Best Employer,
and Earth’s Safest Place to Work. Customer reviews, 1-Click
shopping, personalized recommendations, Prime, Fulfillment by
Amazon, AWS, Kindle Direct Publishing, Kindle, Career Choice, Fire
tablets, Fire TV, Amazon Echo, Alexa, Just Walk Out technology,
Amazon Studios, and The Climate Pledge are some of the things
pioneered by Amazon. For more information, visit amazon.com/about
and follow @AmazonNews.
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