03 Dec AWS Announces Six New Amazon SageMaker Capabilities, Includ…
SEATTLE–(BUSINESS WIRE)–Dec 3, 2019–
Today at AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com company (NASDAQ: AMZN), announced six new Amazon SageMaker capabilities, including Amazon SageMaker Studio, the first fully integrated development environment for machine learning, that makes it easier for developers to build, debug, train, deploy, monitor, and operate custom machine learning models. Today’s announcements give developers powerful new tools like elastic notebooks, experiment management, automatic model creation, debugging and profiling, and model drift detection, and wraps them in the first fully integrated development environment (IDE) for machine learning, Amazon SageMaker Studio. To get started with Amazon SageMaker, visit: https://aws.amazon.com/sagemaker/.
Amazon SageMaker is a fully managed service that removes the heavy lifting from each step of the machine learning process. Tens of thousands of customers utilize Amazon SageMaker to help accelerate their machine learning deployments, including ADP, AstraZeneca, Avis, Bayer, British Airways, Cerner, Convoy, Emirates NBD, Gallup, Georgia-Pacific, GoDaddy, Hearst, Intuit, LexisNexis, Los Angeles Clippers, NuData (a Mastercard Company), Panasonic Avionics, The Globe and Mail, and T-Mobile. Since launch, AWS has regularly added new capabilities to Amazon SageMaker, with more than 50 new capabilities delivered in the last year alone, including Amazon SageMaker Ground Truth to build highly accurate annotated training datasets, SageMaker RL to help developers use a powerful training technique called reinforcement learning, and SageMaker Neo which gives developers the ability to train an algorithm once and deploy on any hardware. These capabilities have helped many more developers build custom machine learning models. But just as barriers to machine learning adoption have been removed by Amazon SageMaker, customers’ desire to utilize machine learning at scale has only increased.
Amazon SageMaker makes a lot of the building block steps to developing great machine learning models much easier. But many times, building truly great models that evolve successfully as a business grows takes a lot of optimizations between these building blocks and requires visibility into what’s working or not and why. These challenges are not unique to machine learning, as the same is true of software development, generally. However, over the past few decades, lots of tools like IDEs that help with testing, debugging, deployment, monitoring, and profiling have been built to help with the challenges faced by software developers. But due to its relative immaturity, these same tools simply haven’t existed in machine learning – until now.
Today’s announcements include significant capabilities that make it much easier for customers to build, train, explain, inspect, monitor, debug, and run custom machine learning models:
“As tens of thousands of customers have used Amazon SageMaker to remove barriers to building, training, and deploying custom machine learning models, they’ve also encountered new challenges from operating at scale, and they’ve continued to provide feedback to AWS on their next set of challenges,” said Swami Sivasubramanian, Vice President, Amazon Machine Learning, AWS. “Today, we are announcing a set of tools that make it much easier for developers to build, train, explain, inspect, monitor, debug, and run custom machine learning models. Many of these concepts have been known and used by software developers to build, test, and maintain software for many years; however, they were not available for developers to build machine learning models. Today, with these launches, we are bringing these concepts to machine learning developers for the very first time.”
Autodesk is a global leader in software for customers in the architecture, engineering/construction, product design, and manufacturing industries. Autodesk’s software offerings include AutoCAD (drafting software) and BIM 360 (cloud platform for project delivery and construction document management). “At Autodesk, we leverage machine learning to enhance our design and manufacturing solutions to enable greater degrees of creative freedom for our customers. Generative design technology can produce hundreds of optimized solutions that meet design criteria,” said Alexander Carlson, Machine Learning Engineer, Autodesk. “Using machine learning, we developed a new filter that identifies and groups outcomes with similar visual characteristics to make it easier to find the best options. This Visual Similarity filter will always be adapting to what it is observing, making it easier and more efficient to find that perfect design. Amazon SageMaker Debugger allows us to iterate on this model much more efficiently by helping close the feedback loop, saving valuable data scientist time, and cutting training hours by more than 75%.“
Change Healthcare is a leading independent healthcare technology company that provides data and analytics-driven solutions to improve clinical, financial, and patient engagement outcomes in the U.S. healthcare system. “At Change Healthcare, we are continuously working with our healthcare providers to remove inefficiencies from the processing of healthcare claims. We often receive claim forms from our healthcare providers which have unreadable labels and fixing these forms manually adds time and cost to the claim settlement process. We have developed a multi-layer deep learning model that superimposes labels from a good form into unreadable forms,” said Jayant Thomas, Senior Director, AI Engineering, Change Healthcare. “Amazon SageMaker Debugger helped us improve the accuracy of the model with rapid iterations which helped us achieve our release milestone. Additionally, SageMaker Debugger is helping us gain deeper insights on tensors, achieve resilient model training, assist in detecting inconsistencies in real time using rule hooks, and tune the model parameters for better accuracy.”
INVISTA is a world leading integrated fiber, resin, and intermediates company. “The new services within Amazon SageMaker are reaping powerful benefits for us at INVISTA. With Amazon SageMaker Studio, we’re now able to co-locate data science tasks. Instead of having to manage many separate resources, our team can easily continue to work in a path with little friction. This…