01Module 7 - ML Pipeline OrchestrationMaster the tools and patterns for orchestrating reliable, production-grade ML pipelines using Airflow, Prefect, Kubeflow, ZenML, and beyond.02ML Pipeline Orchestration ConceptsUnderstand the fundamental concepts behind ML pipeline orchestration - DAGs, dependency management, idempotency, and why cron jobs are a silent disaster for production ML.03Apache Airflow for MLLearn how to use Apache Airflow to orchestrate production ML pipelines - DAG authoring, executors, XCom patterns, and avoiding the most common Airflow pitfalls.04PrefectBuilding and deploying production ML workflows using Prefect 2.x/3.x - flows, tasks, deployments, work pools, and observability.05Kubeflow PipelinesBuilding, compiling, and running production ML pipelines on Kubernetes using Kubeflow Pipelines v2 with MLMD metadata tracking and automatic retraining triggers.06MetaflowBuilding scalable, reproducible ML workflows with Netflix's Metaflow - the flow-step model, cloud compute with @batch and @kubernetes, and Cards for documentation.07ZenMLBuilding portable, stack-agnostic MLOps pipelines with ZenML - stacks, steps, materializers, and seamless local-to-cloud migration with MLflow and Vertex AI.08Choosing an OrchestratorA decision framework for selecting the right ML pipeline orchestrator - comparing Airflow, Prefect, Kubeflow Pipelines, Metaflow, ZenML, and Dagster across team size, maturity, and infrastructure requirements.
