AWS Announces Two New Initiatives That Make Machine Learning More Accessible - Seite 2

Delivered in collaboration with Intel and supported by the talent transformation platform Udacity, the AWS AI & ML Scholarship program allows students from around the world to access dozens of hours of free training modules and tutorials on the basics of machine learning and its real-world applications. 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. 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 can also put their virtual race cars to the test in the new AWS DeepRacer Student League. The AWS DeepRacer Student League helps people of all skill levels learn how to build machine learning models with a fully autonomous 1/18^th scale race car driven by machine learning, a 3D racing simulator, and a global competition. AWS DeepRacer has been used by enterprises like Capital One, BMW, Deloitte, JP Morgan Chase, Accenture, and Liberty Mutual to teach their employees to build, train, and deploy machine learning models in a hands-on way. To get started with the AWS AI & ML Scholarship, visit awsaimlscholarship.com.

Amazon SageMaker Studio Lab provides no-cost access to a machine learning development environment to put machine learning in the hands of everyone

Amazon SageMaker Studio Lab offers a free version of Amazon SageMaker, which is used by researchers and data scientists worldwide to build, train, and deploy machine learning models quickly. Amazon SageMaker Studio Lab removes the need to have an AWS account or provide billing details to get up and running with machine learning on AWS. Users simply sign up with an email address through a web browser, and Amazon SageMaker Studio Lab provides access to a machine learning development environment. Amazon 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 GPU compute for training machine learning models at no cost. 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. 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. Amazon SageMaker Studio Lab can be used as 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. To learn more about Amazon SageMaker Studio Lab, visit aws.amazon.com/sagemaker/studio-lab.