All free events

marimo for AI and ML Development

Enable reactive execution and predictable AI workflows

Sponsored by

Logo: marimo

What you’ll learn—and how you can apply it

  • Articulate why reactive, dependency-aware execution prevents the reproducibility failures endemic to notebooks like Jupyter
  • Build and share reproducible AI/ML experiments using modern environment and dependency tooling alongside marimo’s controlled execution model
  • Develop interactive data exploration and model evaluation workflows with reactive visualizations
  • Apply AI coding agents as active development partners to accelerate prototyping, debug models, and iterate faster across the full ML workflow
  • Package and deploy marimo notebooks as standalone scripts, shareable web apps, or importable Python modules, without rewriting code

Event description

Interactive programming environments are central to modern AI development, but traditional ones like Jupyter notebooks don’t quite fit the bill. Join Parul Pandey to get a hands-on introduction to marimo, the next-generation programming environment designed specifically for AI and ML.

Learn how to do serious computational work in a reproducible environment that also brings data to life in new ways and allows practitioners to rapidly answer difficult questions about data and models—substantially decreasing time to value. You’ll also get a look at other modern tools that every AI and ML developer should adopt, as well as best practices for using AI coding agents for AI/ML development.

This live event is for you because…

  • You’re an AI or ML engineer or data practitioner who writes Python and builds models.
  • You work with data and run experiments, notebooks, and ML workflows in production settings.
  • You’re curious about marimo and want to see how it improves on traditional notebooks.

Prerequisites:

  • A computer with marimo installed, or work in marimo’s free cloud-hosted notebook workspace Molab
  • Intermediate Python proficiency
  • Basic familiarity with data science or ML workflows (loading data, training a model, evaluating results, etc.)
  • Experience with Jupyter notebooks or similar interactive environments

Course preparation

Recommended follow-up: