Перейти к основному содержимому

Machine Learning on AWS

Suman Debnath, Principal Developer Advocate for Machine Learning at AWS, guides you through the essentials of running machine learning workflows on AWS.
Suman Debnath
Suman Debnath

Создать бесплатный аккаунт

Практикуйтесь и следите за своим прогрессом.

Продолжить с GoogleПродолжая, вы принимаете нашу Условия использования, наши Политику конфиденциальности и то, что ваши данные хранятся в США.

Amazon Web Services (AWS) offers powerful tools for building and scaling machine learning models, from traditional analyses to generative AI workflows. Learning how to use Amazon Bedrock and SageMaker not only boosts productivity but also enhances the scalability and reliability of machine learning projects. For data scientists and machine learning engineers, moving analyses to the cloud with AWS opens new possibilities for collaboration and deployment.

In this hands-on code-along session, Suman Debnath, Principal Developer Advocate for Machine Learning at AWS, guides you through the essentials of running machine learning workflows on AWS. You’ll learn how to get started with Amazon Bedrock and SageMaker, explore the full lifecycle of machine learning and generative AI workflows, and discover how to seamlessly transition your analyses to the cloud. This session is ideal for professionals looking to expand their machine learning capabilities and leverage the cloud for greater efficiency and scalability.

Key Takeaways:

  • Learn how to use Amazon Bedrock and SageMaker for machine learning.
  • Understand machine learning and generative AI workflows on AWS.
  • Discover best practices for moving machine learning analyses to the cloud.

Session Resources (including link to GitHub Repo)

Темы

Machine LearningAWS

Связанное

Учебное руководство

The Complete Guide to Machine Learning on AWS with Amazon SageMaker

Показать больше →