ml-pipeline-workflow

Start with a linear pipeline of data ingestion, validation, feature engineering, training, evaluation, and deployment stages. Define dependencies in a DAG using Airflow, Dagster, or Kubeflow, then add validation, monitoring, and gradual rollout stages incrementally.

Airflow suits general DAG-based workflow scheduling, Dagster uses asset-based orchestration with strong typing, and Kubeflow Pipelines targets Kubernetes-native ML workloads. Choose based on your infrastructure and whether you need Kubernetes-native execution.

Implement automated retraining triggered by data drift detection on a schedule or metric threshold. Version datasets with DVC, track experiments in MLflow, and validate new models against baselines before promoting them to deployment.

Yes, the Skill covers canary releases, blue-green deployments, and shadow deployments for model serving. Start with shadow deployments to validate behavior, then use canary rollouts with automated rollback triggers based on latency and accuracy metrics.

Pipeline failures usually stem from unmet stage dependencies, unavailable input data, or non-idempotent components. Check logs at each stage boundary, validate input and output data contracts, and test components in isolation before rerunning.