ml-pipeline-workflow

Define modular stages for data ingestion, validation, feature engineering, training, evaluation, and deployment, then wire them as a DAG with explicit dependencies. Start with a simple linear pipeline and progressively add validation, monitoring, and rollout stages.

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

Use MLflow or Weights & Biases to log metrics, parameters, and artifacts during training jobs, and register approved models in a model registry. TensorBoard can supplement this for visualizing training metrics.

Start with shadow deployments to observe behavior without affecting users, then use canary releases to validate on a small traffic slice. Keep rollback mechanisms and automated triggers ready in case performance degrades.

Failures usually come from unmet stage dependencies, unavailable input data, or schema mismatches at stage boundaries. Check per-stage logs, validate inputs and outputs at each boundary, and test components in isolation.