Orchestrate ML Workflows in Amazon SageMaker & AWS Step Functions

Published on Nov 30, 2018

oin us as we talk about the newly announced features for orchestrating machine learning workflows with Amazon SageMaker and AWS Step Functions. SageMaker enables you to build, train, and deploy machine learning models quickly and easily and at scale. With Step Functions, you can add resilient server less workflows to your applications. Workflows on Step Functions require less code to write and maintain. What if we were to combine the best of SageMaker and Step Functions. Yes, you can now use Step Functions to automate and orchestrate ML workflows with SageMaker in an end-to-end workflow. Step Functions will monitor SageMaker jobs in a seamless experience. Learn more: https://aws.amazon.com/blogs/machine-learning/new-features-for-amazon-sagemaker-workflows-algorithms-and-accreditation/ Continuing on the journey of making it easy for developers, we will also talk about the newly announced SageMaker Search that is available now in beta. With SageMaker Search, you can quickly find and evaluate model training runs from thousands of your model training jobs quickly and easily. Lastly, we will talk about the ability to associate any self hosted Git repository with Amazon SageMaker Notebook instances, making it easy to setup your notebook instances to work with Git repositories. Catch up on the excitement of re:Invent 2018 with the AWS launchpad featuring launch announcements, demos of newly launched technology, interviews with expert guests and live Q&A. AWS re:Invent is a tech education conference for the global cloud computing community hosted by Amazon Web Services. See all recordings of the AWS Launchpad at re:Invent here: https://www.youtube.com/playlist?list=PLhr1KZpdzukc0WXQruGVXTiNPtct-LLaa and learn more about AWS live streaming here: https://aws.amazon.com/twitch.