Open Standards Make MLOps Easier and Silos Harder - MLOps Podcast Episode 234

Explore the importance of open standards in MLOps with Cody Peterson, Senior Technical Product Manager at Voltron Data, in this 46-minute podcast episode. Discover how open-source projects like Ibis and Apache Arrow are revolutionizing data handling, enabling scalability beyond traditional tools like pandas. Learn about the benefits of composable data systems, avoiding vendor lock-in, and keeping costs low. Gain insights into the evolution of data engineering, the role of SQL versus data frames, and the concept of "Open Periphery" in building next-generation data systems. Understand how open standards can break down silos in real-world engineering teams and improve collaboration in machine learning projects.

[] Cody's preferred beverage
[] Takeaways
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[] Cody's work at Azure ML
[] LLM Data Engineering Evolution
[] The Ibis project
[] SQL verse data frames
[] Evolution of Ibis
[] Apache Arrow
[] "Open standards are a good idea"
[] How to create standards for AI quality
[] Network effect
[] "Open Periphery" concept explained
[24:29 - ] WandB Free Courses ad
[] Voltron Data users
[] Choosing data system consideration
[] Smooth transition with Ibis
[] Community requests for Ibis
[] Incorporate new tech wisely
[] Using LLMs for internal Queries
[] Tech news overload
[] BirdBrain explores SQL Series
[] Wrap up