Ramp up your path to AI

AutoAI: The Secret Sauce

On making the data scientist’s life easier

Photo by Sam X on Unsplash

In a recent competition for predicting consumer credit risk, AutoAI beat 90% of the participating data scientists.

AutoAI is a new tool that utilizes sophisticated training features to automate many of the complicated and time-consuming tasks of feature engineering and building machine learning models, without the need to be a pro at data science.

The next video shows a preview of the AutoAI tool. Today’s UI is a bit different than the one in the video, which will be generally available soon. You can try it today here.

Re-create Example Following This Tutorial

AutoAI = Intelligent Automation for AI

AutoAI automatically prepares data, applies algorithms, and builds model pipelines best suited for your data and use case. It combines IBM Research methodologies with Watson Studio’s capabilities.

AutoAI consists on automatic:

Model Selection

Feature Engineering and

Hyperparameter Optimization

Model Selection

For selecting the best model efficiently, AutoAI runs estimators against small subsets of the data. The sample of the data is increased gradually, eliminating estimators in this iterative process. This methodology¹ ensures choosing the best estimator while saving computational time and storage. Up to 31x Speedup.

Results from paper¹ of applying Model Selection method (DAUB) vs full training.

Feature Engineering

Coming up with the right set of features is one of the most time consuming steps of Machine Learning projects. AutoAI’s automatic feature engineering combines the following two techniques:

Pattern learning to extract the most common transformations from thousands of datasets. This step happens offline. A Reinforcement Learning approach² to prune the feature space efficiently. The reward function is the difference between the quality metric evaluated at the new feature space and the quality metric evaluated at the original feature space.

Hyperparameter Optimization

The method³ behind automatic hyperparameter tuning is optimized for costly function evaluations, like training functions with big datasets. It refines the best performing pipelines while converging to a good solution of the non-linear problem involved when searching for optimal parameters.

What used to take days or weeks, only takes minutes.

Why AutoAI is different than other AutoML libraries?