Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

ML Pipeline Workflow Complete end to end MLOps pipeline orchestration from data preparation through model deployment. Overview This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring. When to Use This Skill Building new ML pipelines from scratch Designing workflow orchestration for ML systems Implementing data → model → deployment automation Setting up reproducible training workflows Creating DAG based ML orchestration Integrating ML components into production systems What This Skill Provides Core Capabilities 1. Pipeline Architecture End to end workflow design DAG orchestration patterns (Airflow, Dagster, Kubeflow) Component dependencies and data flow Error handling and retry strategies 2. Data Preparation Data validation and quality checks Feature engineering pipelines Data versioning and lineage Train/validation/test splitting strategies 3. Model Training Training job orchestration Hyperparameter management Experiment tracking integration Distributed training patterns 4. Model Validation Validation frameworks and metrics A/B testing infrastructure Performance regression detection Model comparison workflows 5. Deployment Automation Model serving patterns Canary deployments Blue green deployment strategies Rollback mechanisms Reference Documentation See the references/ directory for detailed guides: data preparation.md Data cleaning, validation, and feature engineering model training.md Training workflows and best practices model validation.md Validation strategies and metrics model deployment.md Deployment patterns and serving architectures Assets and Templates The assets/ directory contains: pipeline dag.yaml.template DAG template for workflow orchestration training config.yaml Training configuration template validation checklist.md Pre deployment validation checklist Usage Patterns Basic Pipeline Setup Production Workflow 1. Data Preparation Phase Ingest raw data from sources Run data quality checks Apply feature transformations Version processed datasets 2. Training Phase Load versioned training data Execute training jobs Track experiments and metrics Save trained models 3. Validation Phase Run validation test suite Compare against baseline Generate performance reports Approve for deployment 4. Deployment Phase Package model artifacts Deploy to serving infrastructure Configure monitoring Validate production traffic Best Practices Pipeline Design Modularity : Each stage should be independently testable Idempotency : Re running stages should be safe Observability : Log metrics at every stage Versioning : Track data, code, and model versions Failure Handling : Implement retry logic and alerting Data Management Use data validation libraries (Great Expectations, TFX) Version datasets with DVC or similar tools Document feature engineering transformations Maintain data lineage tracking Model Operations Separate training and serving infrastructure Use model registries (MLflow, Weights & Biases) Implement gradual rollouts for new models Monitor model performance drift Maintain rollback capabilities Deployment Strategies Start with shadow deployments Use canary releases for validation Implement A/B testing infrastructure Set up automated rollback triggers Monitor latency and throughput Integration Points Orchestration Tools Apache Airflow : DAG based workflow orchestration Dagster : Asset based pipeline orchestration Kubeflow Pipelines : Kubernetes native ML workflows Prefect : Modern dataflow automation Experiment Tracking MLflow for experiment tracking and model registry Weights & Biases for visualization and collaboration TensorBoard for training metrics Deployment Platforms AWS SageMaker for managed ML infrastructure Google Vertex AI for GCP deployments Azure ML for Azure cloud OCI Data Science for Oracle Cloud Infrastructure deployments Kubernetes + KServe for cloud agnostic serving Progressive Disclosure Start with the basics and gradually add complexity: 1. Level 1 : Simple linear pipeline (data → train → deploy) 2. Level 2 : Add validation and monitoring stages 3. Level 3 : Implement hyperparameter tuning 4. Level 4 : Add A/B testing and gradual rollouts 5. Level 5 : Multi model pipelines with ensemble strategies Common Patterns Batch Training Pipeline Real time Feature Pipeline Continuous Training Troubleshooting Common Issues Pipeline failures : Check dependencies and data availability Training instability : Review hyperparameters and data quality Deployment issues : Validate model artifacts and serving config Performance degradation : Monitor data drift and model metrics Debugging Steps 1. Check pipeline logs for each stage 2. Validate input/output data at boundaries 3. Test components in isolation 4. Review experiment tracking metrics 5. Inspect model artifacts and metadata Next Steps After setting up your pipeline: 1. Explore hyperparameter tuning skill for optimization 2. Learn experiment tracking setup for MLflow/W&B 3. Review model deployment patterns for serving strategies 4. Implement monitoring with observability tools Related Skills experiment tracking setup : MLflow and Weights & Biases integration hyperparameter tuning : Automated hyperparameter optimization model deployment patterns : Advanced deployment strategies

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