OpenAI

Databricks brings OpenAI GPT‑5.5 to enterprise agent workflows

Show HN: Mljar Studio – local AI data analyst that saves analysis as notebooks

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. --- top comments --- [MSaiRam10] 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 [hasyimibhar] 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 [2ndorderthought] 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. [amirathi] 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 [trymamboapp] "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.

Serverless Notebooks and Jobs Environment Variables [let's design this together]

**Quick scenario**: you spend time getting your notebook working. `OPENAI_API_KEY` is set, `PIP_EXTRA_INDEX_URL` is pointing to your private registry, everything runs. You click Schedule. The job fails. Env var not found. Should these be set at the workspace, folder, or user? Sound familiar? If so, we should be friends. It's Justin Breese (PM at Databricks) and I am back to chat about dependency management - researching how to make environment variables work seamlessly across serverless notebooks and jobs - so clicking Schedule just works, no extra config, no surprises. Want them to work cross workspace, project, etc.? **I want to talk to you if:** * 🔁 You re-set env vars every session because they don't persist * 💥 You've had a notebook-to-job failure caused by a missing env var * 🔐 Managing API keys or credentials in notebooks feels more manual than it should * 🏢 You're a workspace admin who wants to set shared config (pip registry, endpoints) once for everyone Options: 1. 30 minutes, no prep needed. Grab time here: [https://calendar.app.google/CxxpHKBWvxRVQM7i9](https://calendar.app.google/CxxpHKBWvxRVQM7i9) 2. Email me direct feedback and tons of context: [j@databricks.com](mailto:j@databricks.com) 3. Messenger pigeon: Send one to me? 4. Or just drop a comment - even a "yes this is a pain" tells me something useful. Thanks!

OpenAI GPT-5.5 + Codex, now available and fully-governed in Databricks