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.

// 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.

// MLOps Jobs board
https://mlops.pallet.xyz/jobs

// MLOps Swag/Merch
https://mlops-community.myshopify.com/

// Related Links
Nicolas' Medium - https://medium.com/@nmauti
Data Engineering for AI/ML Conference: https://home.mlops.community/home/events/dataengforai

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Nicolas on LinkedIn: https://www.linkedin.com/in/nicolasmauti/?locale=en_US

Timestamps:
[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