Amazon unveils dozens of machine learning tools

At AWS re:Invent, the company announced new capabilities for Amazon SageMaker, the general availability of Amazon Neptune ML, and more.

Amazon Machine Learning Vice President Swami Sivasubramanian discusses the company’s new machine learning tools.

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Calling machine learning “one of the most disruptive technologies we will ever encounter in our generation,” Amazon Machine Learning Vice President Swami Sivasubramanian introduced  a bevy of new tools Tuesday at AWS re:Invent. There will be nine new Amazon SageMaker capabilities, a HIPAA-eligible service for healthcare and life science organizations called Amazon HealthLake, and general availability for Amazon Neptune ML, he said.

Sivasubramanian also announced previews for Amazon Redshift ML and Amazon Lookout for Metrics. “More than 100,000 customers use AWS for machine learning today. These tools are no longer a niche investment. Our customers are applying machine learning to the core of their business. Our customers are innovating in every industry,” Sivasubramanian said.

“SageMaker includes a broad set of capabilities that are novel and unique,” he said, adding: “Until recently, it was only accessible to the big tech firms and cool startups that needed experts to build these sophisticated ML models. Freedom to invent requires that builders of all skill levels can reap the benefits of revolutionary technology. The technology itself allows for experimentation, failures, and limitless possibilities.”

During the machine learning keynote speech, Sivasubramanian and others spoke about the diversity of industries now incorporating machine learning into their business processes.

Companies like Domino’s have been able to use machine learning to help decrease delivery times while sports leagues like the NFL use it to study and help reduce the number of head injuries. Nike, Formula 1, Roche, and BMW also use machine learning to help predict trends and streamline internal systems.

SageMaker is the most commonly used machine learning system, and Amazon announced a number of updates and add-ons that will help more users incorporate machine learning.

“Today, we are announcing a set of tools for Amazon SageMaker that makes it much easier for developers to build end-to-end machine learning pipelines to prepare, build, train, explain, inspect, monitor, debug, and run custom machine learning models with greater visibility, explainability, and automation at scale,” Sivasubramanian said.

On Tuesday, Amazon unveiled Data Wrangler, which gives SageMaker developers an easier way to get data ready for machine learning. The Feature Store offers users with “a purpose-built data store for storing, updating, retrieving, and sharing machine learning features,” while Clarify offers SageMaker developers more visibility into their training data so they can try to decrease potential bias in some machine learning models.

Amazon also announced the availability of Amazon Neptune ML, which will provide users with a machine learning program built specifically for graph data. The company is also previewing Amazon Redshift ML, a tool that would allow developers to run machine learning algorithms on Amazon Redshift data without manually selecting, building, or training ML models.

For HealthLake, Sivasubramanian explained that it helps healthcare organizations organize their data into a centralized AWS data lake that can be automatically normalized using machine learning.

“There has been an explosion of digitized health data in recent years with the advent of electronic medical records, but organizations are telling us that unlocking the value from this information using technology like machine learning is still challenging and riddled with barriers,” Sivasubramanian explained.

“With Amazon HealthLake, healthcare organizations can reduce the time it takes to transform health data in the cloud from weeks to minutes so that it can be analyzed securely, even at petabyte scale. This completely reinvents what’s possible with healthcare and brings us that much closer to everyone’s goal of providing patients with more personalized and predictive treatment for individuals and across entire populations.”

The solution was built with healthcare systems, pharmaceutical companies, clinical researchers, and health insurers in mind, he said.

AWS works with healthcare enterprises like 3M, Anthem, AstraZeneca, Bristol Myers Squibb, Cerner, the Fred Hutchinson Cancer Research Center, GE Healthcare, Infor, Pfizer, and Philips on various machine learning projects.

In a statement, said Anne O’Hanlon, product director, Orion Health, said: “Data is frequently messy and incomplete, which is costly and time consuming to clean up. We are excited to work alongside AWS to deliver new ways for patients to interact with the healthcare system, supporting initiatives such as the 21st Century Cures Act designed to make healthcare more accessible and affordable, and Digital Front Door, which aims to improve health outcomes by helping patients receive the perfect care for them from the comfort of their home.”

“Expanding the relationship we enjoy with AWS gives us an opportunity to innovate and explore new ways to deliver patient-centered healthcare and high-quality health outcomes that help people live a healthier life,” O’Hanlon said.


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