Recent advances in the theories and designs of deep neural networks, combined with innovative hardware advances, have enabled an exciting period of rapid development and research in artificial intelligence. At the center of this is access to on-demand elastic compute resources via the cloud. Amazon SageMaker is a fully managed machine learning platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models in the cloud, at any scale. SageMaker provides a collection of built-in high-performance algorithms to perform training on petabyte-scale datasets, in a distributed environment.
Randall Hunt offers an overview of SageMaker and explains how the platform is architected to run these algorithms with up to 10x performance boosts for model training. Randall then demonstrates an end-to-end machine learning workflow by building an ML-powered Twitter bot that you can interact with in real time. You’ll leave ready to design, build, train, iterate, and deploy machine learning in a production environment at scale.
All code demonstrated in examples is public and open source.
This session is sponsored by Amazon Web Services.
Randall Hunt is a Los Angeles-based senior technical evangelist and software engineer at Amazon Web Services. Python is his favorite language, but he can sometimes be found in the dark realm of C++. Randall is the author of a number of open source projects and a contributor to MongoDB, Homebrew, boto, and several other tools and libraries. Previously, Randall launched rockets at NASA and SpaceX, but he found his programming passion at MongoDB. He is a total space nerd.
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