MLJAR

MLJAR

MLJAR is an open-source machine learning platform for automated machine learning. It allows users without coding skills to easily build and deploy machine learning models.
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MLJAR: Open-Source Automated Machine Learning Platform

Build and deploy machine learning models without coding skills, using MLJAR's user-friendly interface and automated workflow, an open-source solution for data scientists and non-experts alike.

What is MLJAR?

MLJAR is an open-source automated machine learning platform designed to make machine learning accessible to everyone. It provides a user-friendly interface that allows users without coding skills to easily build, evaluate, compare, tune, interpret and deploy machine learning models.

Some key features of MLJAR include:

  • Automated machine learning - MLJAR automatically tests multiple algorithms and hyperparameters to find the best model for your data.
  • No-code interface - Everything can be done through a graphical interface, no coding required.
  • Comparing models - Compare performance of models side-by-side to select the best one.
  • Model tuning - Tune hyperparameters to optimize model performance.
  • Model interpretation - Understand what factors drive your model's predictions.
  • Model deployment - Deploy models to production as API endpoints or batches.

MLJAR supports classification, regression and time series modeling. It can handle numeric and textual data, images, and more. Models can be exported and used in other Python applications.

Overall, MLJAR makes advanced machine learning simple and accessible to citizen data scientists and domain experts in various industries.

MLJAR Features

Features

  1. Automated machine learning
  2. Intuitive graphical user interface
  3. Support for classification, regression and time series forecasting
  4. Integration with popular data science frameworks like scikit-learn, XGBoost, LightGBM
  5. Model explanation and analysis tools
  6. Model deployment to production

Pricing

  • Open Source

Pros

No coding required

Quickly build accurate models

Visual interface for model building and analysis

Open source and free to use

Cons

Limited flexibility compared to coding models

Less control over model hyperparameters

Limited model deployment options


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