45 min
Klaviyo Data Science Podcast EP 46 | ML Ops 101 Klaviyo Data Science Podcast
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- Marketing
An Introduction to ML Ops
Building data science products requires many things we’ve discussed on this podcast before: insight, customer empathy, strategic thinking, flexibility, and a whole lot of determination. But it requires one more thing we haven’t talked about nearly as much: a stable, performant, and easy-to-use foundation. Setting up that foundation is the chief goal of the field of machine learning operations, aka ML Ops.
This month on the Klaviyo Data Science Podcast, we give a brief but thorough introduction to the field of ML Ops. You’ll hear about:
How ML Ops is different from the similar fields of data science and DevOps
What skills a successful ML Ops developer should have, and what an ML Ops developer’s day-to-day looks like
Why concepts like “velocity” and “stability” have their own special nuances in the world of ML Ops
For the full show notes, including who's who, see the Medium writeup.
An Introduction to ML Ops
Building data science products requires many things we’ve discussed on this podcast before: insight, customer empathy, strategic thinking, flexibility, and a whole lot of determination. But it requires one more thing we haven’t talked about nearly as much: a stable, performant, and easy-to-use foundation. Setting up that foundation is the chief goal of the field of machine learning operations, aka ML Ops.
This month on the Klaviyo Data Science Podcast, we give a brief but thorough introduction to the field of ML Ops. You’ll hear about:
How ML Ops is different from the similar fields of data science and DevOps
What skills a successful ML Ops developer should have, and what an ML Ops developer’s day-to-day looks like
Why concepts like “velocity” and “stability” have their own special nuances in the world of ML Ops
For the full show notes, including who's who, see the Medium writeup.
45 min