David Ping

ML has quickly become a top priority for organizations everywhere, but achieving ML at scale across many business units and teams is no small feat. Join this chalk talk to learn how Amazon SageMaker Feature Store lets you share new ML features, search and discover existing features, and flexibly reuse features in new models to accelerate the pace of ML model development.

Until recently, data scientists have spent much time performing operational tasks, such as ensuring that frameworks, runtimes, and drivers for CPUs and GPUs work well together. In addition, data scientists needed to design and build end-to-end machine learning (ML) pipelines to orchestrate complex ML workflows for deploying ML models in production.

Until recently, data scientists have spent much time performing operational tasks, such as ensuring that frameworks, runtimes, and drivers for CPUs and GPUs work well together. In addition, data scientists needed to design and build end-to-end machine learning (ML) pipelines to orchestrate complex ML workflows for deploying ML models in production.