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Two new Amazon Web Services (AWS) initiatives that make machine learning (ML) more accessible for anybody interested in experimenting and learning with the technology were introduced by Swami Sivasubramanian, Amazon Machine Learning VP, during his AWS re:Invent keynote on Dec. 1, the third day of the conference in Las Vegas.

First up during his presentation, which was streamed live also, was the new Amazon SageMaker Studio Lab, which he said provides everyone access to a no-cost version of Amazon SageMaker, the AWS service that helps customers build, train and deploy ML models.

Then he announced AWS AI & ML Scholarship, a new education and scholarship program that he said will offer $10 million a year in scholarships and internships to “prepare underrepresented and underserved students for careers in machine learning” globally.

The new program uses AWS DeepRacer (the company’s cloud-based, 3D racing simulator) and the new AWS DeepRacer Student League to teach students foundational ML concepts by giving them hands-on experience training ML models for autonomous race cars, while providing educational content focused on ML fundamentals, according to the company.

The “ML Revolution”

The “ML revolution” that we are seeing is “all about how customers are reinventing their business with data,” Sivasubramanian said at the start of the keynote.

“Data is the underlying force that fuels the insights and the predictions that helps you make better decisions and spur completely new innovations,” he told attendees.

ML is “one of the most transformative technologies we will encounter in our generation,” he went on to say, explaining: “It is improving customer experience. It is creating more efficiencies in our operations and spurring completely new innovations. Having the right data strategy is critical to this innovation.”

Currently, “more than 100,000 customers are using AWS for machine learning,” he said. Citing IDC data, he told attendees that “more ML workloads happen on AWS than anywhere else.” Customers select AWS “for our reliability, ease of use and security,” he noted.

But he said: “Making ML more accessible to all is central to creating a more diverse and inclusive tech workforce. With that in mind, today we are launching the AWS AI & ML Scholarship program.”

The program is being offered in collaboration with Intel and supported by the talent transformation platform Udacity, he noted. The initiative builds on the 15-year relationship of AWS and Intel that has been “dedicated to developing, building, and supporting cloud services that are designed to manage cost and complexity, accelerate business outcomes, and scale to meet current and future computing requirements,” AWS said in a news release.

Along with free access to “dozens of hours of free machine learning model training and educational materials, 2,000 qualifying students from underrepresented and underserved communities will win a scholarship for the AI Programming with Python Udacity Nanodegree program, designed to give scholarship recipients the programming tools and techniques fundamental to machine learning,” according to AWS.

Graduates from the initial Nanodegree program will be invited to take a technical assessment and 500 students who receive the highest scores in that assessment will earn a second Udacity Nanodegree program scholarship on deep learning and ML engineering to “help further prepare them for a career in artificial intelligence and machine learning,” the company said. The top 500 students will also have access to mentorship opportunities from tenured Amazon and Intel technology experts for career insights and advice.

“Students can use AWS DeepRacer to turn theory into hands-on action by learning how to train machine learning models to power a virtual race car,” the company said. Those students who successfully complete educational modules by passing knowledge-check quizzes, meet certain AWS DeepRacer lap time performance targets and submit an essay will be considered for Udacity Nanodegree program scholarships.

Students will also be able to enter their virtual race cars in the new AWS DeepRacer Student League that was designed to helps people of all skill levels learn how to build ML models with a fully autonomous 1/18^th scale race car driven by ML, a 3D racing simulator and a global competition, AWS said.

AWS DeepRacer has been used by companies including Accenture, BMW, Capital One, Deloitte, JP Morgan Chase and Liberty Mutual to teach their employees to build, train and deploy ML models in a hands-on way, according to AWS.

More SageMaker Studio Lab Details

Amazon SageMaker Studio Lab, meanwhile, “removes the need to have an AWS account or provide billing details to get up and running with machine learning on AWS,” the company pointed out. Users can sign up with just an email address via  a web browser, and Amazon SageMaker Studio Lab will provide access to a ML development environment, it said.

SageMaker Studio Lab provides unlimited user sessions that include 15 gigabytes of persistent storage to store projects and up to 12 hours of CPU and four hours of graphics processing unit (GPU) compute for training ML models at no cost, according to AWS.

“There are no cloud resources to build, scale, or manage with Amazon SageMaker Studio Lab, so users can start, stop, and restart working on machine learning projects as easily as closing and opening a laptop,” the company said.

“When users are done experimenting and want to take their ideas to production, they can easily export their machine learning projects to Amazon SageMaker Studio to deploy and scale their models on AWS,” it added.

SageMaker Studio Lab can be used as either a “no-cost learning environment for students or a no-cost prototyping environment for data scientists where everyone can quickly and easily start building and training machine learning models with no financial obligation or long-term commitments,” according to AWS.