Ujjwal Ratan shares an end-to-end workflow for building a machine learning (ML) pipeline, keeping fundamental best practices at its core. You’ll learn how to use AWS services like S3, Athena, Glue, Redshift Spectrum, and SageMaker and discover the importance of storing data in a persistent layer and allowing transient compute services to act on it, instead of moving the dataset around. Ujjwal also outlines the cost benefits of utilizing transient compute resources and paying for only the amount of time you use it, opening the door to leverage AWS’s most powerful graphics.
This session is sponsored by AWS.
Ujjwal Ratan is a senior machine learning specialist solution architect on the global healthcare and life sciences team at Amazon Web Services (AWS). Ujjwal has over 13 years of experience serving in technology-enabling roles in the healthcare industry, working with large enterprises and small startups alike to design and implement solutions to solve problems in healthcare and life sciences by applying machine learning. He has been an evangelist for AWS healthcare AI and a vocal supporter for the use of machine learning in the field of healthcare and life sciences and has presented at conferences and published blogs and whitepapers on the topic.
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