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Experiment Tracking for AI Research | Lightning AI Tutorial

Lightning AI9 July 2026Watch on YouTube

Part of series

Ep. 3 · Experiment Tracking met LitLogger

Praktische tutorials over het bijhouden van ML-experimenten met Lightning AI's LitLogger-tool.

View the series

Description

Track every machine learning experiment in one place with Lightning AI's experiment tracking tools, powered by LitLogger. In this tutorial, you'll learn how to integrate experiment tracking into your training workflow, automatically log metrics, hyperparameters, artifacts, environment details, and media outputs, then compare multiple training runs side by side to understand how configuration changes affect model performance. Whether you're training PyTorch models, fine-tuning LLMs, or running computer vision workloads, keep your experiments organized, reproducible, and easy to analyze. Lightning AI brings development, training, experiment tracking, and deployment together in a single platform so you can move from research to production without stitching together multiple tools. 📖 Learn more: https://lightning.ai/docs/overview/experiment-management 🚀 Get started: https://go.lightning.ai/3Rmtk7A

What you'll learn

  • Learn how to automatically log metrics, hyperparameters, and artifacts from your machine learning experiments using LitLogger
  • Understand how to compare multiple training runs side by side to see how configuration changes affect model performance
  • Discover how experiment tracking keeps your experiments organized, reproducible, and easy to analyze in one place
  • See how Lightning AI integrates development, training, experiment tracking, and deployment without stitching together multiple tools

Frequently asked questions

What is LitLogger and what does it automatically log?
LitLogger is Lightning AI's experiment tracking tool that automatically logs metrics, hyperparameters, artifacts, environment details, and media outputs from your training workflow.
How does experiment tracking help when comparing training runs?
Experiment tracking lets you compare multiple training runs side by side to directly see how configuration changes affect model performance.
What types of machine learning projects can you manage with Lightning AI experiment tracking?
You can organize and analyze PyTorch models, fine-tuned LLMs, and computer vision workloads with Lightning AI's experiment tracking.
Why does Lightning AI integrate multiple functions in one platform?
By integrating development, training, experiment tracking, and deployment in one platform, you can move from research to production without stitching together multiple tools.

Topics

experiment trackingmachine learning toolsresearch workflowpytorchmlops