22 AutoML Case Studies / Examples: In-Depth Guide [2019]

Though there is a lot of buzz around autoML, we haven’t found a good compilation of case studies. So we built our comprehensive list of automated machine learning case studies so you can see how autoML could be used in your function/industry.

This AutoML case study list will help us to understand what AutoML is and how you can use it in your business function. The most common application areas of autoML are decision-making and forecasting. Read on to discover how AutoML can support your business function. Read more


AutoML Software / Tools in 2019: In-depth Guide

We explained autoML in detail. Now it is time to figure out the right software for auto ML for your business.

3 Types of AutoML Solution Providers

Open Source

AI is one of the few scientific areas were despite significant corporate investment, even secretive tech giants like Apple publish their research findings. Therefore it should not be surprising that there are competitive open source autoML tools.

All open source tools we came across, need an active development environment in Python or R and require the user to write at least a few lines of code to initiate the automated machine learning process. Read more


AutoML: In depth Guide to Automated Machine Learning [2019]

Automated machine learning has the potential to greatly increase the productivity of data scientist and democratize machine learning tools. It can be a powerful solution to the well documented scarcity of data scientists.

What is automated machine learning?

According to Wikipedia:

Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems.

Automated ML solutions aim to automate some or all steps of the machine learning process which includes:

  • Data pre-processing
  • Feature engineering
  • Feature extraction
  • Feature selection
  • Algorithm selection & hyperparameter optimization

Since accuracy of machine learning solutions can be measured, automated systems can fine-tune data, features, algorithms and hyperparameters of algorithms to generate accurate models relying on established machine learning knowledge and trial&error. Read more