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

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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 - 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 components can be swapped out allowing engineers to use the right tool for the job to be done. It also helps avoid vendor lock-in and keep costs low.
// Bio
Cody is a Senior Technical Product Manager at Voltron Data, a next-generation
data systems builder that recently launched an accelerator-native GPU query
engine for petabyte-scale ETL called Theseus. While Theseus is proprietary,
Voltron Data takes an open periphery approach -- it is built on and interfaces
through open standards like Apache Arrow, Substrait, and Ibis. Cody focuses on the Ibis project, a portable Python dataframe library that aims to be the
standard Python interface for any data system, including Theseus and over 20
other backends.
Prior to Voltron Data, Cody was a product manager at dbt Labs focusing on the open source dbt Core and launching Python models (note: models is a confusing term here). Later, he led the Cloud Runtime team and drastically improved the efficiency of engineering execution and product outcomes.
Cody started his carrer as a Product Manager at Microsoft working on Azure ML. He spent about 2 years on the dedicated MLOps product team, and 2 more years on various teams across the ML lifecycel including data, training, and inferencing.
He is now passionate about using open source standards to break down the silos and challenges facing real world engineering teams, where engineering
increasingly involves data and machine learning.
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// Related Links
Ibis Project: ibis-project.org
Apache Arrow and the “10 Things I Hate About pandas”: wesmckinney.com/blog/apache-a...
Apache Arrow: arrow.apache.org/
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