MLOps

Implement MLOps practices for managing the complete ML lifecycle.

Overview

MLOps applies DevOps principles to machine learning, enabling reliable, scalable, and automated ML systems. This skill covers pipelines, versioning, monitoring, and production workflows.

Quick Reference

Core Principles

1. Automation - Automate training, testing, and deployment
2. Versioning - Track code, data, and models
3. Reproducibility - Ensure consistent results
4. Monitoring - Detect performance degradation
5. Collaboration - Enable team workflows

MLOps Components

Experiment Tracking

Record experiments, metrics, and artifacts.

Tools:

• MLflow: Open-source, comprehensive
• Weights & Biases: Collaborative, feature-rich
• TensorBoard: Visualization-focused

Track:

• Hyperparameters
• Metrics (loss, accuracy)
• Model artifacts
• Code versions

Data Versioning

Version control for datasets.

Tools:

• DVC (Data Version Control)
• Git LFS
• Delta Lake

Benefits:

• Reproducibility
• Lineage tracking
• Collaboration

Model Registry

Centralized model storage and versioning.

Features:

• Version management
• Stage transitions (staging, production)
• Metadata and lineage
• Access control

Tools:

• MLflow Model Registry
• AWS SageMaker Model Registry
• Azure ML Model Registry

CI/CD for ML

Continuous Integration

Automated testing of code and models.

Tests:

• Unit tests for code
• Data validation
• Model performance tests
• Integration tests

Continuous Deployment

Automated model deployment.

Steps:

1. Trigger on model registry update
2. Run validation tests
3. Deploy to staging
4. Run integration tests
5. Deploy to production
6. Monitor performance

Pipeline Orchestration

Automate ML workflows.

Tools:

• Kubeflow Pipelines
• Apache Airflow
• AWS Step Functions
• Azure ML Pipelines

Production Monitoring

Model Performance

Track prediction quality.

Metrics:

• Accuracy, precision, recall
• Latency, throughput
• Error rates

Data Drift

Detect changes in input distribution.

Methods:

• Statistical tests (KS test, Chi-square)
• Distribution comparison
• Feature drift monitoring

Model Drift

Detect degradation in model performance.

Indicators:

• Decreasing accuracy
• Changing prediction distribution
• Increased errors

Response:

• Retrain model
• Update features
• Investigate root cause

Using the Reference Files

/references/tracking.md — Experiment tracking with MLflow and W&B, data versioning with DVC, model registry, and reproducibility practices.

/references/automation.md — CI/CD pipelines for ML, automated training, testing strategies, deployment automation, and orchestration tools.

/references/production.md — Model monitoring, drift detection, automated retraining, A/B testing, canary deployments, and incident response.

Best Practices

• Version everything (code, data, models)
• Automate testing and deployment
• Monitor models in production
• Implement automated retraining
• Use feature stores for consistency
• Document pipelines and processes
• Set up alerting for failures
• Practice reproducibility
• Enable collaboration with tools
• Plan for model updates

Common Pitfalls to Avoid

• Manual deployment processes
• No data versioning
• Insufficient monitoring
• Ignoring data drift
• No automated retraining
• Poor experiment tracking
• Lack of testing
• No rollback strategy
• Inadequate documentation
• Not planning for scale