Module 7: MLOps & Model Management

Learn MLOps practices including model versioning, pipeline automation, model monitoring, and A/B testing.

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MLOps & Model Management

Learn MLOps practices including model versioning, pipeline automation, model monitoring, and A/B testing.

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Model Versioning & Tracking

Learn model versioning strategies, experiment tracking, and reproducibility techniques for machine learning projects.

Content by: Nirav Khanpara

AI/ML Engineer

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Model Versioning Concepts

  • •Model Registry: Centralized model storage and management
  • •Version Control: Track model versions and changes
  • •Artifact Management: Store model files and metadata
  • •Reproducibility: Ensure consistent model training

Experiment Tracking

  • •Hyperparameter Tracking: Log training parameters
  • •Metrics Recording: Track model performance metrics
  • •Code Versioning: Link code versions to experiments
  • •Environment Management: Track dependencies and configurations

MLflow Framework

  • •MLflow Tracking: Experiment tracking and logging
  • •MLflow Models: Model packaging and deployment
  • •MLflow Registry: Model versioning and lifecycle
  • •MLflow Projects: Reproducible ML workflows

Best Practices

  • •Consistent naming conventions
  • •Comprehensive metadata logging
  • •Automated versioning workflows
  • •Regular model validation and testing

🎯 Practice Exercise

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Next Module

Module 8: Big Data & Distributed ML

Additional Resources

📚 Recommended Reading

  • •MLOps Engineering at Scale by Chip Huyen
  • •Building Machine Learning Pipelines by Hannes Hapke
  • •Practical MLOps by Noah Gift

🌐 Online Resources

  • •MLflow Documentation
  • •Kubeflow User Guide
  • •Apache Airflow Tutorial

Ready for the Next Module?

Continue your learning journey and master the next set of concepts.

Continue to Module 8