Show HN: Mljar Studio – local AI data analyst that saves analysis as notebooks (mljar.com)

73 points by pplonski86 ↗ HN
Hi HN,

I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio.

The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun.

What MLJAR Studio does:

- Sets up a local Python environment automatically, runs on Mac, Windows, and Linux

- Installs missing packages during the conversation

- Built-in AutoML for tabular data (classification, regression, multiclass)

- Works with standard Python libraries (pandas, matplotlib, etc.)

- Works with any data file: CSV, Excel, Stata, Parquet ...

- Connects to PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, and Supabase.

For AI: use Ollama locally (zero data egress), bring your own OpenAI key, or use MLJAR AI add-on.

I built this because I wanted something between Jupyter Notebook (flexible but manual) and AI tools that generate code but don’t preserve the workflow. Most tools I tried either hide too much or don’t give reproducible results and are cloud based

Demos:

- 60-second demo: https://youtu.be/BjxpZYRiY4c

- Full 3-minute analysis: https://youtu.be/1DHMMxaNJxI

Pricing is $199 one-time, with a 7-day trial.

Curious if this is useful for others doing real data work, or if I’m solving my own problem here.

Happy to answer questions.

20 comments

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This is one of those product areas I would call high-risk without a human in the loop. So I am glad you kept a person in the loop. It's really easy to lose tons of money making decisions based on bad statistics or models. Anyone remember how much money zillow lost because of automatic time series models?

I do have concerns about the workflow. Data people aren't usually the best programmers. Models hallucinate and make mistakes sometimes subtle sometimes not. Can you think of a way to prevent data scientists from having to be expert code reviewers? I feel like taking away the code gives them the chance to find and fix mistakes in their reasoning but I have no evidence for that.

Human in the loop in data analysis is really challenging task. We provide Python code for inspection, so user can check details how results were produced. Additionally, we run AI on results - user need to check the outputs and AI provided insights.
This is one shot with Claude Code. What’s the moat?
Really cool. If somebody doesn't want to adopt a new platform, take a look at open source Jupyter MCP Server[1]. Once integrated with Claude, it can execute code on the live notebook kernel.

I just let Claude write notebooks, run top to bottom, debug & fix errors & only ping me when everything is working.

[1] https://github.com/datalayer/jupyter-mcp-server

Thanks for sharing! MLJAR Studio was created for people with domain knowledge but not much technical expertise. For them, setting up a Python environment, installing required packages, configuring Jupyter Lab, the MCP server, and Claude Code might be technically demanding.

MLJAR Studio is a desktop application available for Windows, MacOS, and Linux. MLJAR Studio creates a Python environment for the user and installs all required packages. The user can focus on data rather than fighting technical challenges.

IME "real data work" doesn't involve notebooks.
Notebooks as the output format is funny because notebooks are famously bad for reproducibility. Out of order execution, hidden state, etc. You're solving "chat isn't reproducible" with a format that also isn't really
Python notebooks are not reproducible when used by humans. When notebook format is used to store conversations for AI data analysis, it preserves the chat history and is ideal for reproducibility.
How does this compare to open source Deepnote[0]? We use the cloud version (BYOC) at my previous company to replace self-hosted Jupyter notebooks, and it's pretty great.

[0] https://github.com/deepnote/deepnote

The goal of MLJAR Studio is to make it easy to analyze data for people with large domain knowledge but lack of programming skills. We do not focus on notebooks. Python notebook for us is compute and store layer. Our main interface is chat with AI data analyst. The conversation can be opened as classic notebook, but the main UI is simple chat.
You should check them out, their interface pretty much looks like chat nowadays.
"AI saves analysis as notebooks" is fighting the wrong fight ig. The reproducibility issue with notebooks isn't the format. it's out-of-order cell execution and silent kernel state

llm generation makes that worse: the model has no memory of what state existed when it wrote cell 7, and neither does the user.

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