ML Pipeline Workflow

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

# 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

• 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

# 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

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