As machine learning (ML) becomes a larger part of companies’ core business, there is a greater emphasis on reducing the time from model creation to deployment. Automating the build and deployment of machine learning models is becoming common ask for many organisations worldwide.
AWS Step Functions allows you to coordinate several AWS services into a serverless workflow. You can design and run workflows in which the output of one step acts as the input to the next step, and embed error handling into the workflow.
Amazon SageMaker is a fully managed service that provides developers and data scientists with the tools to build, train, and deploy different types of ML models.
This webinar, first broadcast on August 13, we will demonstrate how to create Machine Learning pipeline that includes training and deployment of Machine Learning model using Python code and make this process repeatable.
Why should you attend?
- Learn how to use AWS Step Functions Data Science Python SDK to define Machine Learning pipeline
- Learn how to deploy Machine Learning pipeline
- Learn how to automate SageMaker jobs with the code