#67 Operationalizing Machine Learning with MLOps (with Alessya Visnjic)

In this episode of DataFramed, Adel speaks with Alessya Visnjic, CEO and co-founder of WhyLabs, an AI Observability company on a mission to build the interface between AI and human operators.

Throughout the episode, Alessya talks about the unique challenges data teams face when operationalizing machine learning that spurred the need for MLOps, how MLOps intersects and diverges with different terms such as DataOps, ModelOps, and AIOps, how and when organizations should get started on their MLOps journey, the most important components of a successful MLOps practice, and more.

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[JanekBogucki]

I can see the value in defining MLOps as a set of choices from tools, practices, techniques and mindset options but I would also characterise it iterms of the impact on those using or impacted by the deployed ML models.