Evaluate and Create ML Workflows Visually

Overview

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.

Syllabus

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 learning activities, 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.