MLFlow Training and Certification in Yangon, Myanmar

This training on MLFlow provides a comprehensive understanding of managing the complete lifecycle of machine learning models, including experiment tracking, model packaging, and deployment. Participants will explore MLFlow’s four key components—Tracking, Projects, Models, and Model Registry—and learn how to streamline MLOps workflows using practical, real-world examples.

Introduction and Experiment Tracking

• Session 1: Introduction to MLFlow

  • Overview of MLOps and the role of MLFlow.
  • MLFlow architecture and components: Tracking, Projects, Models, and Model Registry.
  • Installing and configuring MLFlow in local and cloud environments.

• Session 2: MLFlow Tracking

  • Experiment tracking: Parameters, metrics, and artifacts.
  • Integration with ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).

• Session 3: Advanced Experiment Tracking

  • Organizing experiments with tags and nested runs.
  • Automated tracking using autologging features.

Packaging and Managing ML Projects

• Session 4: MLFlow Projects

  • Structuring ML projects with MLFlow Projects.
  • Running and sharing reproducible ML workflows.

• Session 5: MLFlow Models

  • Saving and packaging models with MLFlow Models.
  • Supported flavors (e.g., Scikit-learn, TensorFlow, PyTorch).

• Session 6: MLFlow Model Registry

  • Managing model lifecycles (staging, production, archived).
  • Model versioning and governance.

Deployment and Advanced Integrations

• Session 7: Model Deployment

  • Deployment options: Local, Docker, Kubernetes, and cloud services.

• Session 8: Monitoring and Managing Models

  • Monitoring deployed models for performance and drift.
  • Integration with monitoring tools (e.g., Prometheus, Grafana).

• Session 9: Best Practices and Capstone Project

  • Best practices for MLOps with MLFlow.
  • Capstone project: End-to-end MLFlow workflow implementation.
  • Participant presentations and feedback.

This MLFlow training equips participants with the skills and tools to streamline MLOps workflows and manage machine learning models effectively. Through hands-on sessions, participants gain practical experience to apply MLFlow in production settings, enhancing their organization's machine learning lifecycle management capabilities.