Evaluate and Create ML Workflows Visually
About this course
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.
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What you'll learn
- using visual dashboards to compare machine learning models
- understanding key performance metrics like accuracy and loss
- refactoring code with tools like LightningModules and DataModules
Course objectives
- equip students with skills to evaluate machine learning models visually
- enable students to create maintainable ML workflows
- prepare students for effective collaboration in ML projects
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