MLOps – Bringing CI/CD to Machine Learning with Amazon SageMaker Pipelines

Jan 26, 2022 · Zürich, Switzerland

Dear DevOps community

In the middle of the pre-Christmas season, this top-class Meetup will sweeten the wait for Santa Claus. Daniel Lorch and Gabriel Zylka tell us about the very current topic MLOps.

Amazon SageMaker makes it easy to deploy Machine Learning (ML) models into production. You can create automated workflows to support development of models with scalable infrastructure and continuous integration and continuous delivery (CI/CD) pipelines. With SageMaker, you can prepare massive volumes of data, manage thousands of training experiments, and track lineage for hundreds of model versions. You can share and re-use workflows to recreate or optimize models, helping you scale ML throughout your organization.

MLOps refers to a methodology that is built on applying DevOps practices to machine learning workloads. Like DevOps, MLOps relies on a collaborative and streamlined approach to the machine learning development lifecycle.

Join this meetup with Gabriel Zylka and Daniel Lorch from Amazon Web Services (AWS) to learn about:

- Why MLOps?
- How to establish best practices for MLOps
- How to set up continuous integration and continuous delivery using CI/CD for machine learning
- How to create workflows to automate common machine learning tasks
- Live demo and walk through of sample code

Gabriel Zylka is an Associate Consultant at AWS Professional Services, advising customers on leveraging machine learning and data science expertise and best practices.

Daniel Lorch is a DevOps Consultant at AWS Professional Services, supporting customers with their challenges in DevOps by leveraging automation, Continuous Integration and Continuous Delivery, and engineering best practices.

18:00 - 18:05: Organizer’s welcome messages
18:05 - 19:00: Talk
19:00 - 19:30: Q&A, Discussion

Looking forward seeing you!

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