BigQuery Feature Store // Nicolas Mauti // #255

Aug 23, 2024

Nicolas Mauti, an MLOps Engineer from Lyon, shares his expertise in transforming BigQuery into a powerful feature management system for AI/ML applications. He discusses the challenges of feature versioning, monitoring, and data quality that his team overcame at Malt. The conversation explores how separating feature creation from model coding streamlined their workflows and enhanced performance. Nicolas also emphasizes the importance of effective data lineage tracking and retraining models to ensure consistent accuracy across machine learning projects.

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Intro

00:00 • 3min

Streamlining Machine Learning with BigQuery's Feature Store

03:02 • 2min

Navigating Feature Sharing Challenges in Data Science

05:01 • 11min

Navigating Feature Versioning in Data Systems

16:05 • 6min

Feature Discovery and Monitoring in ML

22:31 • 6min

Monitoring and Retraining Machine Learning Models

28:26 • 10min

Transitioning to BigQuery for Feature Engineering

38:51 • 10min

Exploring Data Engineering and Upcoming Event Insights

48:59 • 2min

Nicolas Mauti is an MLOps Engineer from Lyon (France), Working at Malt.BigQuery Feature Store // MLOps Podcast #255 with Nicolas Mauti, Lead MLOps at Malt.// AbstractNeed 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!// BioNicolas 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.// MLOps Jobs board https://mlops.pallet.xyz/jobs// MLOps Swag/Merchhttps://mlops-community.myshopify.com/// Related LinksNicolas' Medium - https://medium.com/@nmautiData Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai --------------- ✌️Connect With Us ✌️ -------------Join our Slack community: https://go.mlops.community/slackFollow us on Twitter: @mlopscommunitySign up for the next meetup: https://go.mlops.community/registerCatch all episodes, blogs, newsletters, and more: https://mlops.community/Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/Connect with Nicolas on LinkedIn: https://www.linkedin.com/in/nicolasmauti/?locale=en_USTimestamps:[00:00] Nicolas' preferred beverage[00:35] Takeaways[02:25] Please like, share, leave a review, and subscribe to our MLOps channels![02:57] BigQuery end goal[05:00] BigQuery pain points[10:14] BigQuery vs Feature Stores[12:54] Freelancing Rate Matching issues[16:43] Post-implementation pain points[19:39] Feature Request Process[20:45] Feature Naming Consistency[23:42] Feature Usage Analysis[26:59] Anomaly detection in data[28:25] Continuous Model Retraining Process[30:26] Model misbehavior detection[33:01] Handling model latency issues[36:28] Accuracy vs The Business [38:59] BigQuery cist-benefit analysis[42:06] Feature stores cost savings[44:09] When not to use BigQuery[46:20] Real-time vs Batch Processing[49:11] Register for the Data Engineering for AI/ML Conference now![50:14] Wrap up