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MLJAR AutoML

mljar.com

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Spec score 8.3 #8 of 37 in Predictive Analytics Software

What it is

MLJAR AutoML automates supervised machine learning for tabular data, including binary classification, multiclass classification, and regression. Users can work through MLJAR Studio notebooks or use the open-source Python package in scripts and notebooks. Its four modes—Explain, Perform, Compete, and Optuna—have configurable choices for algorithms, validation, metrics, time budget, and explanations. Depending on the selected mode and settings, workflows can preprocess data, select and tune models, engineer and select features, and create ensembles. Experiments include model comparisons, validation results, training times, explanations, and reusable artifacts; summaries are available in Markdown and JSON. Fairness-aware training evaluates models against a chosen metric and threshold and can try sample reweighting, but does not guarantee fairness. The Python package integrates with Mercury for prediction apps supporting individual and batch CSV predictions; 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. AutoML is designed for supervised tabular tasks, and reports depend on task and settings.

Spec sheet

Purpose
MLJAR AutoML automates supervised machine learning for tabular data, including binary classification, multiclass classification, and regression.Read 9 Oct 2026 · mljar.com
Deployment
Generated Mercury apps can be previewed locally, published through MLJAR Platform, or self-hosted with Docker.Read 9 Oct 2026 · mljar.com
Integrations
The Python package integrates with Mercury to generate prediction apps that support single predictions and batch CSV predictions.Read 9 Oct 2026 · mljar.com
Support
MLJAR’s About page invites users to contact the team with questions and says they are happy to help.Read 9 Oct 2026 · mljar.com
Headquarters
Łapy, PolandRead 28 Sep 2026 · mljar.com
Company
MLJAR identifies Aleksandra Płońska as Founder and Executive Director and Piotr Płoński as Founder and CTO.Read 9 Oct 2026 · mljar.com
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.Read 9 Oct 2026 · mljar.com
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.Read 9 Oct 2026 · mljar.com
Models and reports
Experiments include model comparisons, validation results, training times, explanations, and reusable saved artifacts, with structured summaries available in Markdown and JSON.Read 9 Oct 2026 · mljar.com
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.Read 9 Oct 2026 · mljar.com
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.Read 9 Oct 2026 · mljar.com
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.Read 9 Oct 2026 · mljar.com
Ways to use
Users can run AutoML through MLJAR Studio notebooks or use the open-source Python package in scripts and notebooks.Read 9 Oct 2026 · mljar.com

Plans and prices

Free Free 50 prompts / month · 10 published conversations · 1 public Mercury web app Read 9 Oct 2026 · mljar.com
mljar-supervised Python package Free Open-source · MIT license Read 9 Oct 2026 · mljar.com
Pro $20 a month 500 prompts / month · 50 published conversations · 3 public Mercury web apps · 1 private Mercury web app Read 9 Oct 2026 · mljar.com
Business $60 a month 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 $199, paid once one payment · yours forever 1 year of updates · local LLMs with Ollama · bring your own provider keys Read 9 Oct 2026 · mljar.com

Every MLJAR AutoML plan and price →

Strong points and limits

Strong points

  • MIT-licensed Python package costs nothing
  • Offers four configurable training modes
  • Reports can include comparisons, validation, and explanations
  • Mercury apps support single and batch CSV predictions

Limits

  • Designed for supervised tabular tasks
  • Does not support forecasting workflows or time-series modeling
  • Fairness-aware training does not guarantee a fair model

Who it suits

MLJAR AutoML suits users building supervised models for tabular classification or regression who want configurable training modes and experiment reports. It also suits Python users who want to create prediction apps with Mercury and deploy them locally, through MLJAR Platform, or with Docker.

Verdict

MLJAR AutoML provides a free, open-source route to supervised tabular model training and configurable experiment workflows. Reports and explanations vary with the task and settings, and fairness mitigation is not a guarantee.

Where Specifiction places it

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