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Ubuntu AI | Ep 5: Understanding MLOps and Observability

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Send us a text

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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Fetch error

Hmmm there seems to be a problem fetching this series right now. Last successful fetch was on October 01, 2024 03:55 (7M ago)

What now? This series will be checked again in the next day. If you believe it should be working, please verify the publisher's feed link below is valid and includes actual episode links. You can contact support to request the feed be immediately fetched.

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://ppacc.player.fm/legal.

Send us a text

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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