4 docs tagged with "cloud-platforms"

Master the Azure Machine Learning platform for enterprise ML workflows - workspaces, component-based pipelines, managed endpoints, MLflow integration, and responsible AI.

Master 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.

Master 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.

Master 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.