Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it 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. Read more