Executive Development Programme in Practical ML Model Versioning for Data Scientists

This course is tailored for data scientists and machine learning engineers who manage complex ML models in production. Participants will learn essential skills in versioning ML models, including model tracking, version control, and deployment strategies. They will gain hands-on experience using state-of-the-art tools and best practices to ensure model integrity and scalability.

Upon completion, attendees will be able to implement effective model versioning processes, enhance model transparency and reproducibility, and contribute to more reliable and maintainable machine learning systems.

1. 1. Introduction to Model Versioning: Learners will understand the importance of model versioning in machine learning and learn foundational concepts like version control, metadata management, and best practices for tracking model changes. This module will equip learners with the skills to implement basic versioning strategies.
2. 2. Version Control Systems for Models: This module covers the use of version control systems (VCS) for managing ML models, including Git and Docker. Learners will learn how to use VCS to manage model artifacts and experiment configurations, ensuring reproducibility and traceability of model development.
3. 3. Managing Experiment Metadata: In this module, learners will explore the role of metadata in model versioning, including experiment tracking, metrics, and parameters. They will learn how to use tools like MLflow and TensorBoard to manage and analyze experiment data effectively.
4. 4. Automating Model Versioning Processes: This module focuses on automating model versioning workflows using scripts and CI/CD pipelines. Learners will gain hands-on experience with tools like Jenkins and GitHub Actions to streamline the model development and deployment process.
5. 5. Advanced Model Versioning Strategies: This module delves into advanced versioning strategies, including model archiving, branching, and merging. Learners will learn how to manage multiple versions of a model and handle complex deployment scenarios.
6. 6. Model Registry Management: In this module, learners will study the importance of model registries and learn how to manage them effectively. They will cover topics such as model lifecycle management, versioning policies, and deployment strategies.
7. 7. Data Versioning and Its Impact on Models: This module explores the relationship between data versioning and model versioning. Learners will learn how changes in input data can affect model performance and how to manage data versioning to ensure consistent model evaluation.
8. 8. Model Serving and Versioning: This module focuses on serving models in production and managing multiple versions of models in a live environment. Learners will gain practical experience with model serving frameworks like TensorFlow Serving and Seldon Core.
9. 9. Security and Compliance in Model Versioning: This module covers security and compliance considerations in model versioning, including data privacy, model integrity, and regulatory requirements. Learners will learn how to implement security measures and comply with relevant regulations.
10. 10. Case Studies and Best Practices: The final module involves real-world case studies and best practices in model versioning. Learners will analyze successful implementation strategies and discuss challenges and solutions in the field of ML model versioning.