AutoAI Overview (Watson Machine Learning)

The AutoAI graphical tool in Watson Studio automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem.  These model pipelines are created iteratively as AutoAI analyzes your dataset and discovers data transformations, algorithms, and parameter settings that work best for your problem setting.  Results are displayed on a leaderboard, showing the automatically generated model pipelines ranked according to your problem optimization objective.

Service The Watson Studio, Watson Machine Learning, Watson OpenScale, and other supplemental services are not available by default. An administrator must install these services on the IBM Cloud Pak for Data platform. To determine whether a service is installed, open the Services catalog and check whether the service is enabled.

Required service
Watson Machine Learning
Watson Studio
Data format
Tabular: CSV files, with comma (,) delimiter for all types of AutoAI experiments
Connected data from Networked File System (NFS)
Data from supported data connections. For details, refer to AutoAI data use.

Data size Limits on data files correspond to the compute size you choose for an experiment. For details, refer to AutoAI data use.

AutoAI data use

Supported data connections for AutoAI:

Notes for accessing Microsoft Excel sheets:

If you are using a small compute size, these rules apply:

If you are using a large compute size, these rules apply:

Note: If you are connecting to a database as your data source, the configuration of the database affects the performance of accessing the data. By default, AutoAI opens 15 parallel connections to a database to speed up the data download. However, if the configuration of the database does not permit 15 connections, AutoAI rolls back to downloading using 1 connection at a time. Configuring the database to accept more connections will improve the data access performance.

Data operations in AutoAI

When you load data to train an AutoAI experiment, you can load a single data file, or you can join multiple data files that share common keys into a single training data set. For details, refer to:

For data gathered over a specified date/time range (such as stock prices or temperatures), you can create a time series experiment to predict future activity.

AutoAI process

Using AutoAI, you can build and deploy a machine learning model with sophisticated training features and no coding. The tool does most of the work for you.

To view the code that created a particular experiment, or interact with the experiment programmatically, you can save an experiment as a notebook.

The AutoAI process takes data from a structured file, prepares the data, selects the model type, and generates and ranks pipelines so you can save and deploy a model.

AutoAI automatically runs the following tasks to build and evaluate candidate model pipelines:

Understanding the AutoAI process

For additional detail on each of these phases, including links to associated research papers and descriptions of the algorithms applied to create the model pipelines, see AutoAI implementation details.

Data pre-processing

Most data sets contain different data formats and missing values, but standard machine learning algorithms work with numbers and no missing values. AutoAI applies various algorithms, or estimators, to analyze, clean, and prepare your raw data for machine learning. It automatically detects and categorizes features based on data type, such as categorical or numerical. Depending on the categorization, it uses hyper-parameter optimization to determine the best combination of  strategies for missing value imputation, feature encoding, and feature scaling for your data.

Automated model selection

The next step is automated model selection that matches your data.  AutoAI uses a novel approach that enables testing and ranking candidate algorithms against small subsets of the data, gradually increasing the size of the subset for the most promising algorithms to arrive at the best match. This approach saves time without sacrificing performance. It enables ranking a large number of candidate algorithms and selecting the best match for the data.

For information on how to handle automatically-generated pipelines to select the best model, refer to Selecting an AutoAI model.

Automated feature engineering

Feature engineering attempts to transform the raw data into the combination of features that best represents the problem to achieve the most accurate prediction. AutoAI uses a unique approach that explores various feature construction choices in a structured, non-exhaustive manner, while progressively maximizing model accuracy using reinforcement learning. This results in an optimized sequence of  transformations for the data that best match the algorithms of the model selection step.

For more information on AutoAI features, refer to AutoAI feature comparison.

Hyperparameter optimization

Finally, a hyper-parameter optimization step refines the best performing model pipelines. AutoAI uses a novel hyper-parameter optimization algorithm optimized for costly function evaluations such as model training and scoring that are typical in machine learning. This approach enables fast convergence to a good solution despite long evaluation times of each iteration.

Next steps

Use your own data to build an AutoAI model.

Learn more

Parent topic: Analyzing data and building models