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Utilizing AI Ep. 10: Bringing DevOps Principles to MLOps

Stephen Foskett and Andy Thurai discuss the parallels between DevOps and MLOps with Gaetan Castelein of Tecton. We are in the middle of a shift in analytics and software engineering, with DevOps and continuous deployment, and this is colliding with the development of data analytics and big data. Machine Learning allows organizations to handle this explosion of data and build new applications and automate new business processes, but MLOps must be converged with big data and DevOps tooling to make this a reality. One key enabler of this transformation is the creation of an ML feature store, which stores curated features for machine learning pipelines. Feature stores typically enable users to build features, have standardized feature definitions, run models using these curated features, and manage MLOps.

This episode features:

Stephen Foskett, publisher of Gestalt IT and organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett

Andy Thurai, technology influencer and thought leader. Find Andy’s content at theFieldCTO.com and on Twitter at @AndyThurai

Gaetan Castelein, VP of Marketing at Tecton (@TectonAI ). Find Gaetan on Twitter at @GaetCast

About the author

Gestalt IT Staff

Gestalt IT Staff posts are a collective effort, providing the best analysis and commentary from leaders in the fields of virtualization, networking, storage, and desktop engineering.

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