01Module 10: Cloud ML PlatformsMaster AWS SageMaker, Google Vertex AI, Azure ML, Databricks, and cloud cost optimization strategies for production ML systems.02AWS SageMaker for MLOpsMaster the complete AWS SageMaker ecosystem for end-to-end ML workflows - training jobs, pipelines, model registry, feature store, and production inference at scale.03Google Vertex AI for MLOpsMaster the complete Google Vertex AI platform for end-to-end ML workflows - Pipelines, Training, Prediction, Feature Store, Model Registry, Experiments, and production deployment on GCP.04Azure ML for MLOpsMaster the Azure Machine Learning platform for enterprise ML workflows - workspaces, component-based pipelines, managed endpoints, MLflow integration, and responsible AI.05Databricks for MLOpsMaster the Databricks Lakehouse platform for ML - Delta Lake, Unity Catalog, Feature Store, MLflow Model Registry, Model Serving, and Spark-scale feature pipelines for production ML.06Cloud ML Cost OptimizationMaster cloud cost management for ML workloads - spot instance strategies, storage optimization, inference cost reduction, FinOps tooling, and real-world cost reduction from $80K to $31K/month.
