The Challenge of Establishing AI Quality Standards // Cody Peterson // MLOps podcast #234

Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/ Open Standards Make MLOps Easier and Silos Harder // MLOps podcast #234 with Cody Peterson, Senior Technical Product Manager at Voltron Data | Ibis project. Huge thank you to @WeightsBiases for sponsoring this episode. WandB Free Courses - http://wandb.me/courses_mlops Cody highlighted the perennial challenge of creating standards without contributing to a clutter of compliance. He emphasized the importance of obtaining buy-in from a diverse group of organizations, similar to the successful adoption seen with Apache Arrow. The conversation also touched on how entities like OpenAI are informally setting standards through widespread use. Cody's insights underline the key role of collaboration and popularity in establishing effective industry standards. // Abstract MLOps is fundamentally a discipline of people working together on a system with data and machine learning models. These systems are already built on open standards we may not notice -- Linux, git, scikit-learn, etc. -- but are increasingly hitting walls with respect to the size and velocity of data. Pandas, for instance, is the tool of choice for many Python data scientists -- but its scalability is a known issue. Many tools make the assumption of data that fits in memory, but most organizations have data that will never fit in a laptop. What approaches can we take? One emerging approach with the Ibis project (created by the creator of pandas, Wes McKinney) is to leverage existing "big" data systems to do the heavy lifting on a lightweight Python data frame interface. Alongside other open source standards like Apache Arrow, this can allow data systems to communicate with each other and users of these systems to learn a single data frame API that works across any of them. Open standards like Apache Arrow, Ibis, and more in the MLOps tech stack enable freedom for composable data systems, where