marimo for AI and ML development : enabling reactive execution and predictable AI workflows.

Format:

Video

Contributor:

Pandey, Parul, instructor.

O'Reilly (Firm), publisher.

Language:

English

Subjects (All):

Interactive computer systems.

Artificial intelligence.

Machine learning.

Physical Description:

1 online resource (1 video file (2 hr., 25 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Sebastopol, California] : O'Reilly Media, Inc., 2026.

Summary:

Sponsored by marimo 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. 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 This recording of a 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 Review the marimo Reading page (documentation) 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 Recommended follow-up: Explore marimo documentation for guides, examples, and API reference Read Hands-On Machine Learning with Scikit-Learn and PyTorch (book) Take Agentic Coding with Claude Code (live online course with Ken Kousen) Take Unlock AI Superpowers for Data Pros (live online course with Thomas Nield).

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1593769306

Publisher Number:

0642572395582