majiayu000 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
| Scenario | Recommended Approach | Reference File |
|---|---|---|
| Experiment tracking and versioning | MLflow, Weights & Biases, DVC | /references/tracking.md |
| Automated training and deployment | CI/CD pipelines, Kubeflow, Airflow | /references/automation.md |
| Production monitoring and maintenance | Drift detection, retraining, A/B testing | /references/production.md |
Core Principles
- Automation - Automate training, testing, and deployment
- Versioning - Track code, data, and models
- Reproducibility - Ensure consistent results
- Monitoring - Detect performance degradation
- 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:
- Trigger on model registry update
- Run validation tests
- Deploy to staging
- Run integration tests
- Deploy to production
- 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