Machine Learning on AWS
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.
Description
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, will guide 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.
Presenter Bio
Suman helps improve the developer experience of Amazon SageMaker and Amazon Bedrock. He creates content and gives talks to help people learn AWS machine learning tools. Suman is heavily involved in the AWS Developer Community in India. He switched to ML from a background in storage and performance engineering. Previously, Suman was Principal Developer Advocate for Amazon Elastic File System, and was a senior engineer at Toshiba, Broadcom, and NetApp.