Building Scalable, Observable MLOps Systems on Google Cloud

Learn to build production-ready MLOps systems on Google Cloud Platform through this comprehensive conference talk that addresses the critical challenges of deploying machine learning models at scale. Discover why production ML is inherently difficult and explore the four core barriers that prevent successful deployment: infrastructure complexity, tooling fragmentation, model drift, and governance requirements. Master an end-to-end MLOps framework leveraging Google Cloud's managed service stack including Vertex AI, Dataflow, Cloud Run, and BigQuery. Understand how to choose appropriate deployment patterns for real-time, batch, and streaming scenarios based on your specific use case requirements. Examine a detailed automotive case study demonstrating oil change prognostics architecture to see these concepts applied in practice. Dive deep into production observability strategies for monitoring both model performance and system health, including comprehensive coverage of drift detection, data skew identification, and performance feedback loops. Explore infrastructure telemetry implementation and incident response procedures for maintaining reliable ML systems. Learn to implement CI/CD pipelines specifically designed for machine learning models, including progressive rollout strategies, continuous monitoring, and automated rollback mechanisms. Understand governance frameworks, automated retraining pipelines, and human-in-the-loop approval processes essential for enterprise-grade MLOps. Gain practical insights into scaling reliable MLOps systems through proven blueprints and best practices demonstrated throughout this technical presentation.