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Ubuntu AI | Ep 5: Understanding MLOps and Observability

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Manage episode 380566788 series 3522664
Content provided by Canonical. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Canonical 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://player.fm/legal.

In this episode we're hosting Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack.
Machine learning operations (MLOps) is a new practice that ensures ML workflow automation in a scalable and efficient manner. But how do you make MLOps observable? How can you better understand how your product-grade AI initiative and its infrastructure are performing?

This is where observability comes in.

Learn more about our solutions here: https://ubuntu.com/ai

  continue reading

19 episodes

Artwork
iconShare
 
Manage episode 380566788 series 3522664
Content provided by Canonical. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Canonical 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://player.fm/legal.

In this episode we're hosting Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack.
Machine learning operations (MLOps) is a new practice that ensures ML workflow automation in a scalable and efficient manner. But how do you make MLOps observable? How can you better understand how your product-grade AI initiative and its infrastructure are performing?

This is where observability comes in.

Learn more about our solutions here: https://ubuntu.com/ai

  continue reading

19 episodes

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