ML Pipeline Workflow

Overview

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

• 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

• 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

Copy
# 1. Define pipeline stages
stages = [
    "data_ingestion",
    "data_validation",
    "feature_engineering",
    "model_training",
    "model_validation",
    "model_deployment"
]

# 2. Configure dependencies
# See assets/pipeline-dag.yaml.template for full example

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
• Kubernetes + KServe for cloud-agnostic serving

Progressive Disclosure

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

Copy
# See assets/pipeline-dag.yaml.template
stages:
  - name: data_preparation
    dependencies: []
  - name: model_training
    dependencies: [data_preparation]
  - name: model_evaluation
    dependencies: [model_training]
  - name: model_deployment
    dependencies: [model_evaluation]
`### Real-time Feature Pipeline`
# Stream processing for real-time features
# Combined with batch training
# See references/data-preparation.md
`### Continuous Training`
# Automated retraining on schedule
# Triggered by data drift detection
# See references/model-training.md

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

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