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Klaviyo Data Science Podcast EP 46 | ML Ops 101

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Manage episode 411638972 series 3251385
Content provided by Klaviyo Data Science Team. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Klaviyo Data Science Team or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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.

  continue reading

58 episodes

Artwork
iconShare
 
Manage episode 411638972 series 3251385
Content provided by Klaviyo Data Science Team. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Klaviyo Data Science Team or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

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.

  continue reading

58 episodes

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