Versioning and MLOps for Generative AI

Yucheng Low on developing tools for enhanced versioning, reproducibility, and collaboration in machine learning and AI.

Subscribe: AppleSpotify OvercastGoogleAntennaPodPodcast AddictAmazon •  RSS.

Yucheng Low, Cofounder & CEO of  XetHub, discusses the challenges of managing large-scale machine learning assets and the need for version control. He highlights the importance of tracking changes and collaborating on data and models, and how XetHub’s platform addresses these challenges by providing a versioning system for models and data with collaboration capabilities. The platform supports various file types, including images and unstructured data, and has open-sourced its client surface area for easy integration. The conversation also touches on the challenges of data deletion and the importance of openness and not being locked into a single format.

Subscribe to the Gradient Flow Newsletter


    ❛ MLOps is essentially DevOps, but at a grander scale. If we can scale DevOps to handle repositories of any size and if our Continuous Integration (CI) systems can efficiently manage tasks like GPU-based training, many issues naturally resolve themselves. This would eliminate the need for many tools we currently use. We’re seeing a convergence where machine learning teams are evolving to resemble microservice teams. Instead of teams working solely on one microservice, they work on or integrate with multiple ML model services sourced from various places. Much like microservices, an organization could have dozens or even hundreds of these model services. ❜
    Yucheng Low, Cofounder & CEO of XetHub

Interview highlights – key sections from the video version:


Related content:

If you enjoyed this episode, please support our work by encouraging your friends and colleagues to subscribe to our newsletter: