Experimentation to Industrialization: Implementing MLOps

May 26, 2021 12:05 PM (PT)

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In this presentation, drawing upon Thorogood’s experience with a customer’s global Data & Analytics division as their MLOps delivery partner, we share important learnings and takeaways from delivering productionized ML solutions and shaping MLOps best practices and organizational standards needed to be successful.

We open by providing high-level context & answering key questions such as “What is MLOps exactly?” & “What are the benefits of establishing MLOps Standards?”

The subsequent presentation focuses on our learnings & best practices. We start by discussing common challenges when refactoring experimentation use-cases & how to best get ahead of these issues in a global organization. We then outline an Engagement Model for MLOps addressing: People, Processes, and Tools. ‘Processes’ highlights how to manage the often siloed data science use case demand pipeline for MLOps & documentation to facilitate seamless integration with an MLOps framework. ‘People’ provides context around the appropriate team structures & roles to be involved in an MLOps initiative. ‘Tools’ addresses key requirements of tools used for MLOps, considering the match of services to use-cases.

In this session watch:
Al McEwan, Head of Capability Development, Thorogood Associates
Deb Lee, Senior Consultant, Thorogood Associates

 

Al McEwan

Al McEwan is a principal consultant at Thorogood Associates, a Databricks partner since 2018. He is also a Databricks Champion. Al has been heavily involved with our Databricks partnership since its i...
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Deb Lee

Deb is a motivated, passionate, and impact-focused senior consultant at Thorogood Associates, a Databricks partner since 2018. She has extensive experience leading end-to-end engagements for data engi...
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