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More Data Science with Less Engineering: ML Infrastructure at Netflix

In this Amazon Web Services (AWS) re:Invent session, Netflix shares its human-centric design principles that provides its engineers with autonomy. Learn how Netflix developed an end-to-end machine learning (ML) infrastructure, Metaflow, using elastic compute, high-throughput storage, and dynamic, scalable notebooks built on AWS using Amazon Simple Storage Service (Amazon S3), Amazon SageMaker, AWS Batch, and Amazon Relational Database Service (Amazon RDS).

AWS Services Used

Amazon S3

Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance.

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Amazon SageMaker

Build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows

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AWS Batch

AWS Batch lets developers, scientists, and engineers efficiently run hundreds of thousands of batch and ML computing jobs while optimizing compute resources, so you can focus on analyzing results and solving problems.

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Amazon RDS

Amazon Relational Database Service (Amazon RDS) is a collection of managed services that makes it simple to set up, operate, and scale databases in the cloud.

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