# Notebooks Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/notebooks
> Markdown URL: https://aitinkerers.org/technologies/notebooks.md
> Technology record last updated: 2026-02-25T05:31:13Z
> Generated: 2026-09-21T04:44:00Z

JupyterLite delivers a full JupyterLab environment as a static site: no server, no installation, just WebAssembly in the browser.

JupyterLite transforms the browser into a complete data science workstation by leveraging Pyodide and Xeus-Python kernels. It serves standard .ipynb files via a single page application (SPA) hosted on static providers like GitHub Pages or S3. This architecture eliminates backend maintenance costs: users get immediate access to NumPy, Matplotlib, and Scikit-learn without configuring a remote kernel. It is the gold standard for distributing interactive documentation and reproducible research at zero infrastructure overhead.

- Official technology site: https://jupyterlite.readthedocs.io
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Agents for Data Engineers in the Customer Data Platform Space](https://seattle.aitinkerers.org/talks/rsvp_XNA6bMwaGgE)

A big opportunity for agents to provide a lot of value is for data engineers that use platforms like Databricks. Databricks is a general platform that you can do almost anything on, but it's a bit like a blank canvas. A major use case for enterprise companies is using it as the primary layer for managing all of their customer data. This typically means using other ETL frameworks or writing lots of scripts. Agents can offer a way to start from the value or use case and help data engineers plan how to implement it, and then do that implementation for you. Instead of generating code, it can generate notebooks, configures various applications, etc. I've been working on a project to bring that to life and create an agent focused on helping data engineers implement customer data use cases on Databricks and with SAAS applications.

- Event context: AI Tinkerers Seattle - April Meetup — 2025-04-25 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_XNA6bMwaGgE

### [ZenCoder - Convert your scripts into MLOps pipelines](https://munich.aitinkerers.org/talks/rsvp_d9dijtVwpU0)

One of the first jobs of somebody entering MLOps is to convert their manual scripts or notebooks into pipelines that can be deployed on the cloud. This job is tedious and can take time. Frameworks like [ZenML](https://github.com/zenml-io/zenml) go a long way in alleviating this burden by abstracting much of the complexity away. However, recent advancements in Large Language Model-based Copilots offer hope that even more repetitive aspects of this task can be automated. Unfortunately, most open source or proprietary models like GitHub Copilot are often lagging behind the most recent versions of ML libraries, therefore giving erroneous our outdated syntax when asked simple commands. This project aims to fine-tune an open-source LLM that performs better than off-the-shelf solutions in giving the right output for the latest version of ZenML. Just to make it a bit more fun, we're going to be building ZenML pipelines to achieve this task! That way we write ZenML pipelines to train a model that can produce ZenML pipelines 🐍! Sounds fun!

- Event context: AI Tinkerers Munich - January 18 — 2024-01-18 — Munich
- Public talk page: https://munich.aitinkerers.org/talks/rsvp_d9dijtVwpU0

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