Explore the potential of BigQuery as a feature store for AI/ML applications in this insightful 51-minute podcast episode featuring Nicolas Mauti, Lead MLOps at Malt. Discover how to leverage existing tools to create an effective feature management system, covering topics such as feature table design, monitoring, alerting, data quality, point-in-time lookups, and backfilling. Learn about the advantages and challenges of using BigQuery compared to traditional feature stores, and gain valuable insights into freelancing rate matching, feature request processes, naming consistency, and usage analysis. Delve into practical aspects of MLOps, including anomaly detection, continuous model retraining, handling latency issues, and balancing accuracy with business needs. Evaluate the cost-benefit analysis of BigQuery and explore scenarios where alternative solutions might be more appropriate. This comprehensive discussion also touches on real-time vs. batch processing, providing a well-rounded perspective on implementing BigQuery as a feature store for AI/ML projects.
BigQuery as a Feature Store for AI/ML Applications
Overview
Syllabus
[] Nicolas' preferred beverage
[] Takeaways
[] Please like, share, leave a review, and subscribe to our MLOps channels!
[] BigQuery end goal
[] BigQuery pain points
[] BigQuery vs Feature Stores
[] Freelancing Rate Matching issues
[] Post-implementation pain points
[] Feature Request Process
[] Feature Naming Consistency
[] Feature Usage Analysis
[] Anomaly detection in data
[] Continuous Model Retraining Process
[] Model misbehavior detection
[] Handling model latency issues
[] Accuracy vs The Business
[] BigQuery cist-benefit analysis
[] Feature stores cost savings
[] When not to use BigQuery
[] Real-time vs Batch Processing
[] Register for the Data Engineering for AI/ML Conference now!
[] Wrap up
Taught by
MLOps.community
