Marimo: Reactive Python Notebooks for Reproducible & Fast Data Workflows

Marimo positions itself as next-generation Python notebooks — reactive, reproducible, and engineered for modern data workflows.

In this session, Bartosz Mikulski, our Senior ML Engineer, demonstrates how Marimo addresses long-standing issues in Jupyter-based environments: merge conflicts, hidden global state, non-deterministic execution, fragile cell order, and the constant need for throwaway helper code.

What you’ll learn:
⚙️ how reactive execution and DAG-based state remove hidden state issues
📄 why Python-backed notebooks make version control and merging painless
📊 how to build dynamic UI with sliders, dropdowns, data previews, and interactive charts
🚀 how Marimo integrates with Polars, DuckDB, and SQL for fast analytics
🌐 how to turn your notebook into a clean, stakeholder-ready web app
🧠 caching, async, AI-assisted code generation, embeddings exploration (Fashion-MNIST, LEGO dataset)

If you work with Python notebooks daily, this walkthrough offers a grounded look at tools designed for reproducible, maintainable, engineer-friendly workflows.

00:00 Intro&Agenda
03:19 Marimo vs. Jupyter
06:35 Demo of Marimo
13:01 Data Exploration in Marimo
28:20 Final Thoughts

Check our website: https://deepsense.ai/
Linkedin:   / applied-ai-insider  

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