What is Automated Machine Learning?
mljar AutoML framework

Automated Machine Learning is the end-to-end process
of applying machine learning in an automatic way

Automated Machine Learning

Automated Machine Learning is the end-to-end process of applying machine learning in an automatic way. The complete AutoML pipeline usually consists of: data preprocessing, feature engineering, feature selection, model training, hyperparameter tuning, algorithm selection.

The outlined steps can be very time-consuming. There is a lot of ML algorithms that can be applied at each step of the analysis. The difficulty in manual construction of ML pipeline lays in the difference between data formats, interfaces and computational-intensity of the Machine Learning algorithms. The Automated Machine Learning solution aims to solve this problem by checking automatically different combinations of the ML algorithms. The process of automated machine learning is controlled by statistical algorithm.

The Example Automated Machine Learning Stack

Advantages of Automated Machine Learning

Time icon

Saves the time

The AutoML saves a lot of time for Data Scientist. You don't need to manually preprocess the data, convert between formats, check for the ML packages versions and track ML hyperparameters & experiments.

Categorical data type


The most of ML pipeline is automated. You don't need to worry about manually setting the ML experiments and results tracking. The feature engineering, hyperparameters tuning, model training and documentation - all done in automated manner. Enjoy the results!

Datetime data type


The mljar AutoML framework provides state-of-the-art performance. Use advanced feature engineering, ensemble building and model stacking to build the most accurate ML systems. All in the automated way.

Compare AutoML frameworks

Compare the popular AutoML frameworks on challenging Kaggle datasets
on binary, multiclass classification, and regression Machine Learning tasks.

Will AutoML replace data scientists?

The AutoML will replace data scientists that are not using AutoML tools with the ones that do.
The data scientist with AutoML tools is more productive and can deliver results much faster.
Data Scientist
What is more, the AutoML enables many of the software developers and data analysts
without classical ML training to use Machine Learning methods in the organizations to solve real problems.

Check more mljar features

Golden Features

K-Means Features

Model Ensembling

ML Explainability

Automatic Documentation