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MLJAR AutoML
Łapy, Poland
What it is
MLJAR AutoML automates supervised machine learning for tabular data, covering binary and multiclass classification and regression. Users can run it in MLJAR Studio notebooks or use its open-source Python package in scripts and notebooks. Four modes—Explain, Perform, Compete, and Optuna—let users configure algorithms, validation, metrics, time budgets, and explanations. Depending on the mode and settings, the workflow can preprocess data, select and tune models, engineer and select features, and create ensembles. Experiments can include model comparisons, validation results, training times, explanations, and saved artifacts; structured summaries are available in Markdown and JSON. Fairness-aware training evaluates models against a selected metric and threshold and may attempt sample reweighting, but it does not guarantee fair results. The Python package integrates with Mercury for apps that handle single predictions or batch CSV predictions. These apps can be previewed locally, published through MLJAR Platform, or self-hosted with Docker. The package is open source under the MIT license and costs nothing.
For and against
For
- Automates supervised tabular machine learning.
- Offers four configurable training modes.
- Creates experiment summaries in Markdown and JSON.
- Supports local, platform, and Docker app deployment.
Against
- Designed for supervised tabular tasks.
- Reports and explanations vary by task and settings.
- Fairness-aware training does not guarantee a fair model.
The verdict
MLJAR AutoML combines model-building automation with configurable training modes and experiment reports. Its scope is supervised tabular work, and available reporting depends on the task and configuration.
Who it suits
It suits users building supervised models from tabular data who want configurable automation and experiment summaries. It is not designed for forecasting or time-series modeling.
Plans and prices
Free
50 prompts / month · 10 published conversations · 1 public Mercury web app
Read 9 Oct 2026 · mljar.com
mljar-supervised Python package
Open-source · MIT license
Read 9 Oct 2026 · mljar.com
Pro
500 prompts / month · 50 published conversations · 3 public Mercury web apps · 1 private Mercury web app
Read 9 Oct 2026 · mljar.com
Business
2,000 prompts / month · 200 published conversations · 10 public Mercury web apps · 3 private Mercury web apps
Read 9 Oct 2026 · mljar.com
Perpetual license
1 year of updates · local LLMs with Ollama · bring your own provider keys
Read 9 Oct 2026 · mljar.com
On the record
- Automated workflow
- AutoML can preprocess data, select and tune models, engineer and select features, and create ensembles when enabled by the selected mode and configuration.mljar.com · read 9 Oct 2026
- Company
- MLJAR identifies Aleksandra Płońska as Founder and Executive Director and Piotr Płoński as Founder and CTO.mljar.com · read 9 Oct 2026
- Deployment
- Generated Mercury apps can be previewed locally, published through MLJAR Platform, or self-hosted with Docker.mljar.com · read 9 Oct 2026
- Fairness
- Fairness-aware training evaluates models against a chosen metric and threshold and can try mitigation through sample reweighting, but does not guarantee a fair model.mljar.com · read 9 Oct 2026
- Integrations
- The Python package integrates with Mercury to generate prediction apps that support single predictions and batch CSV predictions.mljar.com · read 9 Oct 2026
- Models and reports
- Experiments include model comparisons, validation results, training times, explanations, and reusable saved artifacts, with structured summaries available in Markdown and JSON.mljar.com · read 9 Oct 2026
- Notable limits
- MLJAR AutoML is designed for supervised tabular tasks, and the available reports and explanations depend on the task, mode, algorithms, and explanation settings.mljar.com · read 9 Oct 2026
- Purpose
- MLJAR AutoML automates supervised machine learning for tabular data, including binary classification, multiclass classification, and regression.mljar.com · read 9 Oct 2026
- Security and privacy
- The privacy policy says MLJAR uses SSL/TLS encrypted connections, access controls, and authentication mechanisms, and advises users not to submit sensitive or regulated data to AI features unless authorized.mljar.com · read 9 Oct 2026
- Support
- MLJAR’s About page invites users to contact the team with questions and says they are happy to help.mljar.com · read 9 Oct 2026
- Training modes
- The four modes are Explain, Perform, Compete, and Optuna, with defaults that users can configure for algorithms, validation, metrics, time budget, and explanations.mljar.com · read 9 Oct 2026
- Ways to use
- Users can run AutoML through MLJAR Studio notebooks or use the open-source Python package in scripts and notebooks.mljar.com · read 9 Oct 2026
