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MLOps Patterns

A comprehensive skill for implementing machine learning operations workflows, from model training and deployment to monitoring and retraining pipelines. This skill provides production-ready patterns for operationalizing ML systems at scale.

Status: 🔵 Master Plan Available

Key Topics​

  • Model Lifecycle Management

    • Version control for models and datasets
    • Experiment tracking and reproducibility
    • Model registry patterns
    • A/B testing and canary deployments

  • Deployment Patterns

    • Batch vs. real-time inference
    • Model serving architectures
    • Containerization and orchestration
    • Edge deployment strategies

  • Monitoring & Observability

    • Model performance metrics
    • Data drift detection
    • Prediction latency tracking
    • Error analysis and debugging

  • Pipeline Automation

    • CI/CD for ML systems
    • Automated retraining workflows
    • Feature store integration
    • Data validation pipelines

Primary Tools & Technologies​

  • Experiment Tracking: MLflow, Weights & Biases, Neptune
  • Model Serving: TensorFlow Serving, TorchServe, Seldon Core, KServe
  • Orchestration: Kubeflow, Airflow, Prefect, Metaflow
  • Monitoring: Evidently AI, WhyLabs, Fiddler
  • Feature Stores: Feast, Tecton, Hopsworks
  • Infrastructure: Kubernetes, Docker, Ray, AWS SageMaker, Azure ML

Integration Points​

  • Data Engineering: Pipeline integration, data quality validation
  • API Design: Model endpoint design, versioning strategies
  • Observability: Metrics integration, logging patterns
  • Security: Model access control, data privacy
  • Testing: Model validation, integration testing

Common Workflows​

Model Deployment Pipeline​

1. Model Training → Experiment Tracking
2. Model Validation → Performance Benchmarks
3. Model Registration → Version Control
4. Deployment Strategy → A/B Test or Canary
5. Monitoring Setup → Drift Detection
6. Feedback Loop → Retraining Triggers

Production Inference​

1. Feature Engineering → Real-time or Batch
2. Model Loading → Cache Management
3. Prediction Serving → Latency Optimization
4. Result Logging → Performance Tracking
5. Error Handling → Fallback Strategies

Best Practices​

  • Separate model training from serving infrastructure
  • Implement comprehensive logging for debugging
  • Monitor both model performance and system metrics
  • Use shadow deployments for validation
  • Automate rollback procedures
  • Version everything (data, code, models, configs)
  • Implement feature flags for gradual rollouts
  • Establish clear model retirement policies

Success Metrics​

  • Deployment Velocity: Time from training to production
  • Model Performance: Accuracy, precision, recall in production
  • System Reliability: Uptime, latency, error rates
  • Data Quality: Drift detection, validation pass rates
  • Resource Efficiency: Cost per prediction, GPU utilization
  • Team Productivity: Experiment-to-deployment ratio