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.
In this webinar 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.
Ievgen Goichuk - Senior Software Engineer at Refinitiv Labs
Ievgen is a technology enthusiast from the past 20 years and is a software engineer with Refinitiv Labs team in London. He is a Computer Science graduate and has been designing and delivering software solutions for numerous clients in Finance, E-Commerce, and Media for over two decades. His expertise includes real-time distributed systems, Cloud engineering, Machine Learning engineering, DLT.
Why you should 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.