BigQuery Feature Store // MLOps Podcast #255 with Nicolas Mauti, Lead MLOps at Malt.
Nicolas kicks things off by discussing his unique morning habits and his preference for orange juice and mint syrup over coffee. We then dive into the technical core of the episode, where Nicolas explains the processes involved in continuously retraining models at Malt. He emphasizes the role of conditional checks, manual initiations for new models, and the monthly training schedule that ensures their models are up-to-date and effective. Crucially, Nicolas highlights the use of Airflow for managing these tasks, from scheduling to feature management, extensively explaining how this system facilitates the robust MLOps strategy at Malt.
// Abstract
Need a feature store for your AI/ML applications but overwhelmed by the multitude of options? Think again. In this talk, Nicolas shares how they solved this issue at Malt by leveraging the tools they already had in place. From ingestion to training, Nicolas provides insights on how to transform BigQuery into an effective feature management system.
We cover how Nicolas' team designed their feature tables and addressed challenges such as monitoring, alerting, data quality, point-in-time lookups, and backfilling. If you’re looking for a simpler way to manage your features without the overhead of additional software, this talk is for you. Discover how BigQuery can handle it all!
// Bio
Nicolas Mauti is the go-to guy for all things related to MLOps at Malt. With a knack for turning complex problems into streamlined solutions and over a decade of experience in code, data, and ops, he is a driving force in developing and deploying machine learning models that actually work in production.
When he's not busy optimizing AI workflows, you can find him sharing his knowledge at the university. Whether it's cracking a tough data challenge or cracking a joke, Nicolas knows how to keep things interesting.
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