Evaluate and Create ML Workflows Visually

This course teaches you how to evaluate machine learning experiments visually and how to transform prototype scripts into reusable, maintainable workflows. You’ll start by exploring how to use visual dashboards like TensorBoard to compare model variants using metrics such as accuracy curves, loss trajectories, and compute usage. Then, you’ll learn how to refactor model training code into standardized structures using tools like LightningModules and DataModules. Through short videos, readings, hands-on Learnings and a final assessment, you’ll gain confidence in comparing models, understanding experiment performance, and creating workflows that your entire team can use. Whether you're presenting model trade-offs or preparing code for a shared repository, you’ll walk away ready to support real-world ML development with clarity and rigor

• Refactor training code into standardized structures with LightningModules and DataModules
• Compare models and understand experiment performance using visual dashboards
• Create workflows usable by entire ML development teams

• Basic familiarity with machine learning concepts and terminology
• Willingness to engage in practical exercises and case studies