Amazon SageMaker Canvas expands access to
machine learning by providing business analysts the ability to
generate more accurate machine learning predictions using a
point-and-click interface—no coding required
Amazon SageMaker Ground Truth Plus offers a
fully managed data labeling service that uses a highly skilled
workforce and built-in workflows to deliver high-quality annotated
data for training machine learning models faster at lower cost
Amazon SageMaker Studio now makes data
engineering, analytics, and machine learning workflows accessible
within a universal notebook
Amazon SageMaker Training Compiler helps
customers train deep learning models up to 50% faster by
automatically compiling code to make it more efficient
Amazon SageMaker Inference Recommender
automatically suggests the optimal AWS compute instances for
running machine learning inference with the best price
performance
Amazon SageMaker Serverless Inference offers
serverless compute for machine learning inference at scale
Today, at AWS re:Invent, Amazon Web Services, Inc. (AWS), an
Amazon.com, Inc. company (NASDAQ: AMZN), announced six new
capabilities for its industry-leading machine learning service,
Amazon SageMaker, that make machine learning even more accessible
and cost effective. Today’s announcements bring together powerful
new capabilities, including a no-code environment for creating
accurate machine learning predictions, more accurate data labeling
using highly skilled annotators, a universal Amazon SageMaker
Studio notebook experience for greater collaboration across
domains, a compiler for machine learning training that makes code
more efficient, automatic compute instance selection machine
learning inference, and serverless compute for machine learning
inference. To get started with Amazon SageMaker, visit
aws.amazon.com/sagemaker.
Driven by the availability of virtually infinite compute
capacity, a massive proliferation of data in the cloud, and the
rapid advancement of the tools available to developers, machine
learning has become mainstream across many industries. For years,
AWS has focused on making machine learning more accessible to a
broader audience of customers. Today, Amazon SageMaker is one of
the fastest growing services in AWS history with tens of thousands
of customers, including AstraZeneca, Aurora, Capitol One, Cerner,
Discovery, Hyundai, Intuit, Thomson Reuters, Tyson, Vanguard, and
many more customers who use the service to train machine learning
models of all sizes, some of which on the extreme now consist of
billions of parameters capable of making hundreds of billions of
predictions every month. As customers further scale their machine
learning model training and inference on Amazon SageMaker, AWS has
continued to invest in expanding the service’s capability,
delivering more than 60 new Amazon SageMaker features and
functionalities in the past year alone. Today’s announcements build
on these advancements to make it even easier to prepare and gather
data for machine learning, train models faster, optimize the type
and amount of compute needed for inference, and expand machine
learning to an even broader audience.
- Amazon SageMaker Canvas no-code machine learning
predictions: Amazon SageMaker Canvas expands access to machine
learning by providing business analysts (line-of-business employees
supporting finance, marketing, operations, and human resources
teams) with a visual interface that allows them to create more
accurate machine learning predictions on their own—without
requiring any machine learning experience or having to write a
single line of code. As more companies seek to reinvent their
businesses and customer experiences with machine learning, more
people in their organizations need to be able to use advanced
machine learning technology across different lines of business.
However, machine learning has typically required specialized skills
that can require years of formal education or intensive training
with a challenging and evolving curriculum. Amazon SageMaker Canvas
solves this challenge by providing a visual, point-and-click user
interface that makes it easy for business analysts to generate
predictions. Customers point Amazon SageMaker Canvas to their data
stores (e.g. Amazon Redshift, Amazon S3, Snowflake, on-premises
data stores, local files, etc.), and the Amazon SageMaker Canvas
provides visual tools to help users intuitively prepare and analyze
data. Amazon SageMaker Canvas then uses automated machine learning
to build and train machine learning models without any coding.
Business analysts can review and evaluate models in the Amazon
SageMaker Canvas console for accuracy and efficacy for their use
case. Amazon SageMaker Canvas also lets users export their models
to Amazon SageMaker Studio, so they can share them with data
scientists to validate and further refine their models.
- Amazon SageMaker Ground Truth Plus expert data labeling:
Amazon SageMaker Ground Truth Plus is a fully managed data labeling
service that uses an expert workforce with built-in annotation
workflows to deliver high-quality data for training machine
learning models faster and at lower cost with no coding required.
Customers need increasingly larger datasets that are correctly
labeled to train ever more accurate models and scale their machine
learning deployments. However, producing large datasets can take
anywhere from weeks to years and often requires companies to hire a
workforce and create workflows to manage the process of labeling
data. In 2018, AWS launched Amazon SageMaker Ground Truth to make
it easier for customers to produce labeled data using human
annotators through Amazon Mechanical Turk, third-party vendors, or
their own private workforce. Amazon SageMaker Ground Truth Plus
expands on this capability with a specialized workforce with
specific domain and industry expertise, as well as qualifications
to meet customers’ data security, privacy, and compliance
requirements for highly accurate data labeling. Amazon SageMaker
Ground Truth Plus has a multistep labeling workflow that includes
pre-labeling powered by machine learning models, machine validation
of human labeling to detect errors and low-quality labels, and
assistive labeling features (e.g. 3D cuboid snapping, removal of
distortion in 2D images, predict-next in video labeling, and
auto-segment tools) to reduce the time required to label datasets
and help reduce the cost of procuring high-quality annotated data.
To get started, customers simply point Amazon SageMaker Ground
Truth Plus to their data source in Amazon Simple Storage Service
(Amazon S3) and provide their specific labeling requirements (e.g.
instructions for how medical experts should label anomalies in
radiology images of lungs). Amazon SageMaker Ground Truth Plus then
creates a data labeling workflow and provides dashboards that allow
customers to follow data annotation progress, inspect samples of
completed labels for quality, and provide feedback to generate
high-quality data so customers can build, train, and deploy highly
accurate machine learning models more quickly.
- Amazon SageMaker Studio universal notebooks: A universal
notebook for Amazon SageMaker Studio (the first complete IDE for
machine learning) provides a single, integrated environment to
perform data engineering, analytics, and machine learning. Today,
teams across different data domains want to collaborate using a
range of data engineering, analytics, and machine learning
workflows. The practitioners of these domains often cross areas of
knowledge like data engineering, data analytics, and data science
and want to be able to work across the various workflows without
needing to switch data exploration tools. However, when customers
are ready to integrate data across analytics and machine learning
environments, they often have to juggle multiple tools and
notebooks, which can be cumbersome, time consuming, and prone to
error. Amazon SageMaker Studio now allows users to interactively
access, transform, and analyze a wide range of data for multiple
purposes all from within a universal notebook. With built-in
integration with Spark, Hive, and Presto running on Amazon EMR
clusters and data lakes running on Amazon S3, customers can now use
Amazon SageMaker Studio to access and manipulate data in a
universal notebook without having to switch services. In addition
to developing machine learning models using their preferred
framework (e.g. TensorFlow, PyTorch, or MXNet) to build, train, and
deploy machine learning models in Amazon SageMaker Studio,
customers can browse and query data sources, explore metadata and
schemas, and start processing jobs for analytics or machine
learning workflows—without leaving the universal Amazon SageMaker
Studio notebook.
- Amazon SageMaker Training Compiler for machine learning
models: Amazon SageMaker Training Compiler is a new machine
learning model compiler that automatically optimizes code to use
compute resources more effectively and reduce the time it takes to
train models by up to 50%. Today's state-of-the-art deep learning
models are so large and complex that they require specialized
compute instances to accelerate training and can consume thousands
of hours of graphical processing unit (GPU) compute time to train a
single model. To further accelerate training times, data scientists
typically try to augment training data or tune hyperparameters
(variables that govern the machine learning training process) to
find the best performing and least resource-intensive version of a
model. This work is technically complicated, and data scientists
often do not have time to optimize the frameworks needed to train
models to run on GPUs. Amazon SageMaker Training Compiler is a new
machine learning model compiler that is integrated with the
versions of TensorFlow and PyTorch in Amazon SageMaker that have
been optimized to run more efficiently in the cloud, so data
scientists can use their preferred frameworks to train machine
learning models through more efficient use of GPUs. With a single
click, Amazon SageMaker Training Compiler automatically optimizes
the trained model and compiles it to execute training up to 50%
faster.
- Amazon SageMaker Inference Recommender automatic instance
selection: Amazon SageMaker Inference Recommender helps
customers automatically select the best compute instance and
configuration (e.g. instance count, container parameters, and model
optimizations) to power a particular machine learning model. For
large machine learning models commonly used for natural language
processing or computer vision, selecting a compute instance with
the best price performance is a complicated, iterative process that
can take weeks of experimentation. Amazon SageMaker Inference
Recommender removes the guesswork and complexity of determining
where to run a model and can reduce the time to deploy from weeks
to hours by automatically recommending the ideal compute instance
configuration. Data scientists can use Amazon SageMaker Inference
Recommender to deploy the model to one of the recommended compute
instances, or they can use the service to run a performance
benchmark simulation across a range of selected compute instances.
Customers can review benchmark results in Amazon SageMaker Studio
and evaluate the tradeoffs between different configuration settings
including latency, throughput, cost, compute, and memory.
- Amazon SageMaker Serverless Inference for machine learning
models: Amazon SageMaker Serverless Inference offers
pay-as-you-go pricing inference for machine learning models
deployed in production. Customers are always looking to optimize
costs when using machine learning, and this becomes increasingly
important for applications that have intermittent traffic patterns
with long idle times. For example, applications like personalized
recommendations based on consumer purchase patterns, chatbots
fielding incoming customer calls, and forecasting demand based on
real-time transactions can have peaks of activity based on external
factors like weather conditions, promotional offerings, or
holidays. Providing just the right amount of compute for machine
learning inference is a difficult balancing act. In some cases,
customers over-provision capacity to accommodate peak activity,
which allows for consistent performance but wastes money when there
is no traffic. In other cases, customers under-provision compute to
constrain costs at the expense of providing enough compute capacity
to perform inference when conditions change. Some customers try
manually adjusting computing resources on the fly to accommodate
changing conditions, but this is tedious and manual work. Amazon
SageMaker Serverless Inference for machine learning automatically
provisions, scales, and turns off compute capacity based on the
number of inference requests. When customers deploy their machine
learning model into production, they simply select the serverless
deployment option in Amazon SageMaker, and Amazon SageMaker
Serverless Inference manages compute resources to provide the
precise amount of compute needed. With Amazon SageMaker Serverless
Inference, customers only pay for the compute capacity they use for
each request and the amount of data processed, without having to
manage the underlying infrastructure.
“Customers across all industries and sizes are excited about how
Amazon SageMaker has helped them scale their use of machine
learning such that it has become a core part of their operations
and allows them to invent new products, services, and experiences
for the world,” said Bratin Saha, Vice President of Amazon Machine
Learning at AWS. “We’re excited to expand our industry-leading
machine learning service to an even broader group of customers, so
they too can drive innovation in their business and help solve
challenging problems. With these new Amazon SageMaker tools, we’re
introducing a whole new group of users to the service while also
providing additional capabilities for existing customers to make it
easier to transform data into valuable insights, accelerate time to
deployment, improve performance, and save money throughout the
machine learning journey.”
The BMW Group, headquartered in Munich, Germany, is a global
manufacturer of premium automobiles and motorcycles, covering the
brands BMW, BMW Motorrad, MINI, and Rolls-Royce. It also provides
premium financial and mobility services. “The use of artificial
intelligence as a key technology is an integral element in the
process of digital transformation at the BMW Group. The company
already employs AI throughout the value chain, enabling it to
generate added value for customers, products, employees, and
processes. In the past few years, we have industrialized many top
BMW Group use cases, measured by business value impact,” said Marc
Neumann, Product Owner, AI Platform at The BMW Group. “We believe
Amazon SageMaker Canvas can add a boost to our AI/ML scaling across
the BMW Group. With SageMaker Canvas, our business users can easily
explore and build ML models to make accurate predictions without
writing any code. SageMaker also allows our central data science
team to collaborate and evaluate the models created by business
users before publishing them to production.”
Siemens Energy is energizing society. They are transforming in
key focus areas of environmental, social, and governance (ESG) and
their innovation is making the future of tomorrow different today,
for both their partners—and their people. “The core of our data
science strategy at Siemens Energy is to bring the power of machine
learning to all business users by enabling them to experiment with
different data sources and machine learning frameworks without
requiring a data science expert. This enables us to increase the
speed of innovation and digitalization of our energy solutions such
as Dispatch Optimizer and Diagnostic services,” said Davood Naderi,
Data Science Team Lead at Industrial Applications for Siemens
Energy. “We found Amazon SageMaker Canvas a great addition to the
Siemens Energy machine learning toolkit, because it allows business
users to perform experiments while also sharing and collaborating
with data science teams. The collaboration is important because it
helps us productionalize more ML models and ensure all models
adhere to our quality standards and policies.”
Airbnb is one of the world’s largest marketplaces for unique,
authentic places to stay and things to do, offering over 7 million
accommodations and 40,000 handcrafted activities, all powered by
local hosts. “At Airbnb, we are increasingly integrating ML across
all aspects of our business. As a result, our teams consistently
need to generate and maintain high-quality data in order to train
and test ML models,” said Wei Luo, Data Scientist at Airbnb China.
“We were looking for a way to generate high quality text
classification data results on one hundred thousand paragraphs of
customer service logs in Mandarin so we can better serve our
customers and reduce dependencies on our customer service team.
With Amazon SageMaker Ground Truth Plus, the AWS team built a
customized data labeling workflow, which included a customized ML
model that was able to achieve 99% classification accuracy.”
The National Football League is America's most popular sports
league, comprised of 32 franchises that compete each year to win
the Super Bowl, the world's biggest annual sporting event. “At the
NFL, we continue to look for new ways to use machine learning to
help our fans, broadcasters, coaches, and teams benefit from deeper
insights,” said Jennifer Langton, SVP, Player Health and Innovation
at NFL. “Football is a fast moving sport where plays can happen in
a split second. While coaches and referees carefully watch the
game, it can be difficult to watch all players on a field for
safety. Computer vision allows us to accurately detect player
safety incidents, but developing these algorithms requires expertly
labeled data. Now with Amazon SageMaker Ground Truth Plus, we have
custom workflows and user interfaces for sophisticated labeling
tasks, which helps us improve player safety.”
Founded and headquartered in Orange County, California, VIZIO’s
mission is to deliver immersive entertainment and compelling
lifestyle enhancements that make its products the center of the
connected home. VIZIO is driving the future of televisions through
its integrated platform of cutting-edge Smart TVs and powerful
SmartCast operating system. VIZIO’s platform gives content
providers more ways to distribute their content and advertisers
more tools to target and dynamically serve ads to a growing
audience that is increasingly transitioning away from linear TV.
“At VIZIO, we consistently look for ways to leverage ML to create
personalized experiences for our customers. We were looking for a
way to continuously review ad videos and generate commercial
metadata for efficient ads classification,” said Zeev Neumeier,
Chief Innovation Officer at VIZIO. “With the use of Amazon
SageMaker Ground Truth Plus’s streaming capability, we can now use
a custom template which provides video classification, metadata
collection, and an automated system that enables data collection in
real time as ads air. With Amazon SageMaker Ground Truth Plus, we
are able to review the results in less than one business day.”
Litterati is a data science company empowering people to
‘crowdsource-clean’ the planet. Litterati’s platform empowers
people to create better solutions for the litter and waste problems
our world faces by developing behavioral insight, mapping problem
areas, and mitigating future risk. From schools to scientists,
environmental organizations, brands, and city governments, people
are coming together using Litterati for the greater good to create
a litter-free world. “For us, machine learning brings light to
unseen challenges. In the US alone, each year billions of dollars
are spent cleaning up litter,” said Sean Doherty, CTO at Litterati.
“With computer vision models, we transform images of litter all
around the world into data, so cities can better allocate their
litter management resources. However, building object detection
models requires access to object, material, and brand information,
as well as localized knowledge due to datasets being spread across
the globe. Amazon SageMaker Ground Truth Plus allows us to create a
hierarchical annotation interface that captures these precise
features within that localized context. In addition, the SageMaker
Ground Truth Plus expert workforce created localized image
annotations, which provides a standardized solution increasing our
data labeling efficiency by up to 20%, accelerating our ability to
ingest annotated results into our database by 200%, and reducing
post-processing time by 90%.”
Provectus helps its customers build end-to-end data and machine
learning engineering experiences from raw datasets, enterprise data
lakes, and machine learning models. "We have been waiting for a
feature to create and manage Amazon EMR clusters directly from
Amazon SageMaker Studio so that our customers could run Spark,
Hive, and Presto workflows directly from Amazon SageMaker Studio
notebooks," said Stepan Pushkarev, CEO at Provectus. "We are
excited that Amazon SageMaker has now natively built this
capability to simplify management of Spark and machine learning
jobs. This will help our customers’ data engineers and data
scientists collaborate more effectively to perform interactive data
analysis and develop machine learning pipelines with EMR-based data
transformations."
The Vanguard Group, Inc., is an American registered investment
advisor based in Malvern, Pennsylvania, with about $7 trillion in
global assets under management. Vanguard is redefining the industry
by doing what’s right for investors and creating change for
millions of clients worldwide. “We’re excited that our Vanguard
data scientists and data engineers can now collaborate in a single
notebook for analytics and machine learning,” said Doug Stewart,
Senior Director of Data and Analytics at Vanguard. “Now that Amazon
SageMaker Studio has built-in integrations with Spark, Hive, and
Presto all running on Amazon EMR, our development teams can be more
productive. This single development environment will allow our
teams to focus on building, training, and deploying machine
learning models.”
Quantum Health is on a mission to make healthcare navigation
smarter, simpler, and more cost-effective for everyone. They use
Amazon SageMaker for use cases like text classification, text
summarization, predictive models, classification problems, and
Q&A to help the Quantum team and the members they serve.
“Iterating with NLP models can be a challenge because of their
size. Long training times bog down workflows and high costs can
discourage our team from trying larger models that might offer
better performance,” said Jorge Lopez Grisman, Senior Data
Scientist at Quantum Health. “Amazon SageMaker Training Compiler is
exciting because it has the potential to alleviate these frictions.
Achieving a speedup with Amazon SageMaker Training Compiler is a
real win for our team that will make us more agile and innovative
moving forward.”
Guidewire is the platform property and casualty insurers trust
to engage, innovate, and grow efficiently. The company combines
digital, core, analytics, and AI to deliver its platform as a cloud
service, and it enables its customers to do advanced analytics and
machine learning for their industry-specific workloads. More than
450 insurers, from new ventures to the largest and most complex in
the world run on Guidewire. “One of Guidewire’s services is to help
customers develop cutting-edge NLP models for applications like
risk assessment and claims operations. Amazon SageMaker Training
Compiler is compelling because it offers time and cost savings to
our customers while developing these NLP models,” said Matt
Pearson, Principal Product Manager, Analytics and Data Services at
Guidewire Software. “We expect it to help us reduce training time
by more than 20% through more efficient use of GPU resources. We
are excited to implement Amazon SageMaker Training Compiler in our
NLP workloads, helping us to accelerate the transformation of data
to insight for our customers.”
Musixmatch is a leading music data company providing data,
tools, and services that enrich the way we experience music such as
searching for songs and sharing song lyrics. Musixmatch is the
largest service of this kind in the world with over 80 million
users and over 8 million distinct lyrics. “Musixmatch uses Amazon
SageMaker to build natural language processing and audio processing
models, and is experimenting using Hugging Face with Amazon
SageMaker. We choose Amazon SageMaker because it allows data
scientists to iteratively build, train, and tune models quickly
without having to worry about managing the underlying
infrastructure, which means data scientists can work more quickly
and independently,” said Loreto Parisi, AI Engineering Director at
Musixmatch. “As the company has grown, so too have our requirements
to train and tune larger and more complex NLP models. We are always
looking for ways to accelerate training time while also lowering
training costs which is why we are excited about Amazon SageMaker
Training Compiler. SageMaker Training Compiler provides more
efficient ways to use GPUs during the training process and, with
the seamless integration between SageMaker Training Compiler,
PyTorch, and high-level libraries like Hugging Face, we have seen a
significant improvement in training time of our transformer-based
models going from weeks to days as well as lower training
costs.”
Loka, a machine learning consulting firm, helps its clients
harness and build ML into their products across a wide range of use
cases to deliver better customer experiences. “We spend a lot of
time and effort optimizing models, tuning servers, and testing
instance types to deliver performant, scalable, and cost effective
ML environments for its client,” said Bobby Mukherjee, CEO at Loka.
“Now using Amazon SageMaker Inference Recommender, our engineers
are able to get an ML model deployed to production within minutes
from any location.”
Holmusk, a digital health company, launched its FoodDX app to
help people improve their diet and health. “Our food image
recognition algorithms need low latency to ensure our users get the
right diet recommendations at the right time. To achieve low
latency, we were over-provisioning GPUs, which was expensive,” said
Sai Subramanian, CTO at Holmusk. “Using Amazon SageMaker Inference
Recommender, we can now easily conduct load tests across different
instances and determine an instance configuration within hours to
reduce our compute costs significantly while maintaining latency
requirements. This is a huge win for our team and lets our ML
scientists focus on creating algorithms to help people live
healthier lives rather than managing infrastructure.”
Qualtrics is an experience management company that helps extract
information from customer surveys using natural language processing
(NLP) models. "Amazon SageMaker Inference Recommender improves the
efficiency of our MLOps teams with the tools required to test and
deploy machine learning models at scale,” said Samir Joshi, ML
Engineer at Qualtrics. “With Amazon SageMaker Inference
Recommender, our team can define latency and throughput
requirements and quickly deploy these models faster, while also
meeting our budget and production criteria."
iFood, a leading player in online food delivery in Latin America
fulfilling over 60 million orders each month, uses machine learning
to make restaurant recommendations to its customers ordering
online. “We have been using Amazon SageMaker for our machine
learning models to build high-quality applications throughout our
business," said Ivan Lima, Director of Machine Learning and Data
Engineering at iFood. "With Amazon SageMaker Serverless Inference,
we expect to be able to deploy even faster and scale models without
having to worry about selecting instances or keeping the endpoint
active when there is no traffic. With this, we also expect to see a
cost reduction to run these services.”
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 81 Availability Zones within 25 geographic regions,
with announced plans for 27 more Availability Zones and nine more
AWS Regions in Australia, Canada, India, Indonesia, Israel, New
Zealand, Spain, Switzerland, and the United Arab Emirates. 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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