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MLJAR Studio

Local desktop notebook with an AI data analyst and AutoML agent — plain-English questions, real Python, data never leaves your machine

freemiumFree: all core features with no time limit, 50 prompts/month, 10 published conversations, 1 public Mercury web app. Pro $20/month: 500 prompts/month, 50 published conversations, 3 web apps. Business and a perpetual lifetime license are also offered; every plan includes the full AI Data Analyst, AutoML, AutoLab, and AI-assisted notebook feature set.View full pricing →

Visit MLJAR Studio

https://mljar.com

About MLJAR Studio

MLJAR Studio is a desktop notebook environment with an AI data analyst built in, aimed at analysts and researchers who want conversational data work without shipping their data to a cloud service. You ask a question in plain English and the assistant generates Python, runs it locally, and shows the result as charts and tables — but every line of generated code stays visible and editable, and the output is a normal reproducible notebook rather than an opaque chat transcript. No external APIs are required, and data stays on your machine, which is the reason academic labs and regulated teams show up in the customer list. On top of the analyst sits an AutoML layer: an experiment agent that improves a notebook step by step, testing ideas and searching for better models, automatically tuning hyperparameters, discovering useful engineered features, comparing and tracking experiments, and generating explanations and reports. The company also ships MLJAR AutoML, the Mercury framework for turning notebooks into shareable web apps, SuperTree for interactive decision-tree visualization, and AutoLab for experiment management, and Studio pulls those together. All plans share the same AI and ML capability set — paid tiers only raise usage limits — and a perpetual license is offered for teams that would rather own the software than rent it.

Key Features

Plain-English questions generate real Python that runs locally
Every generated line of code is visible, editable, and reproducible
AutoML experiment agent that tunes models and engineers features
Experiment comparison, tracking, explanations, and reports
No external APIs required — data stays on your computer
Mercury web-app publishing and SuperTree decision-tree visualization
Perpetual license option alongside subscriptions

Tags

notebooksautomlpythondata analysislocal-firstmachine learning
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