7. TensorFlow Serving for Scalable Deployment

ML is only useful if deployed. Géron teaches how to export models with the SavedModel format, serve them with TensorFlow Serving, and create robust inference endpoints via REST or gRPC. You’ll learn how to structure versioning, integrate with CI/CD pipelines, and scale with Docker or Kubernetes. There’s also a focus on monitoring model performance in production and detecting model drift. The key takeaway: notebooks are for research, but real value comes from production-grade APIs that interface with business systems. Model delivery is as important as model design.