IBM Watson Studio AutoAI versus AutoML

What is AutoML?

Automated machine learning (AutoML) is the process of automating the manual tasks that data scientists must complete as they build and train machine learning models (ML models). These tasks include feature engineering and selection, choosing the type of machine learning algorithm; building an analytical model based on the algorithm; hyperparameter optimization, training the model on tested data sets and running the model to generate scores and findings. Researchers developed AutoML to help data scientists build predictive models without having deep ML model expertise. AutoML also frees data scientists from the rote tasks involved in building a machine learning pipeline, allowing them to focus on extracting the insights needed to solve important business problems.

What is AutoAI?

AutoAI is a variation of AutoML. It extends the automation of model building to the entire AI lifecycle. Like AutoML, AutoAI applies intelligent automation to the steps of building predictive machine learning models. These steps include preparing data sets for training; identifying the best type of model for the given data, such as a classification or regression model; and choosing the columns of data that best support the problem the model is solving, known as feature selection. Automation then tests a variety of hyperparameter tuning options to reach the best result as it generates, and then ranks, model-candidate pipelines based on metrics such as accuracy and precision. The best performing pipelines can be put into production to process new data and deliver predictions based on the model training.

Capability Comparison

Integrates with

AutoAI

AutoML

Data preparation

Feature engineering

Hyperparameter optimization

Automated model deployment

One-click deployment

Model testing and scoring

Code generation

Debiasing and drift mitigation

Model risk management

AI lifecycle management

Transfer learning caching

Any AI models
Advanced data refinery