Model Deployment
Techniques for deploying ML models into production.
Chapter 15: Deployment Strategies Batch inference, real-time inference, hybrid approaches REST APIs, gRPC Deployment patterns: Blue-Green, Canary Chapter 16: Model Serving Tools: TensorFlow Serving, TorchServe, ONNX Runtime Serverless inference with AWS Lambda Chapter 17: Containerization for ML Docker, Kubernetes Building lightweight containers with Buildpacks Chapter 18: Cloud-Based MLOps AWS SageMaker, Google Vertex AI, Azure ML Managed services vs. custom setups Cloud spend optimization for ML workloads (New subtopic) Chapter 19: Multi-Cloud and Hybrid Cloud Strategies (New) Cross-cloud ML pipelines Vendor-agnostic MLOps frameworks [Tools: Kubeflow, Flyte; Multi-cloud orchestration, portability]