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 inception, and was a strong proponent for Thorogood to initiate a partnership and for our customers to embrace Databricks. Al has delivered Databricks engagements at Thorogood’s enterprise customers including Mars, GSK and Unilever. He helped Mars with their Databricks environment set-up and initial empowerment and training. He has been consistently involved over the past couple of years in projects at GSK, including with Pharma Supply Chain, that leverage Databricks for data engineering and data science workloads. Al is involved as a senior member of our project team collaborating with Unilever on industrializing ML use-cases and defining MLOps standards. He has presented at numerous external-facing marketing events for Thorogood and partners.
May 26, 2021 12:05 PM PT
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.