Explore a comprehensive talk on enhancing machine learning reliability in production environments. Learn about common failure modes in large-scale ML systems and discover best practices for productionization. Gain insights into monitoring systems, protecting against human error, ensuring data integrity, and managing pipeline workloads efficiently. Understand the challenges of ML in production, including binary and configuration changes, data updates, and pipeline backlogs. Apply an outside-in approach to ML reliability, drawing from experiences with a large-scale ML production platform at Google.