In earlier sections we saw how IBM SPSS Modeler and Watson Studio Modeler flows allow you
to graphically create a stream or flow that includes data transformation steps and machine
learning models.
Such sequences of steps are called data pipelines or ML pipelines.
This section examines a feature of Watson Studio that helps to automate the creation
of machine learning pipelines.
This allows data scientists to produce results much faster and to focus on more creative
work.
There is currently a shortage of qualified data scientists.
Many operations that a data scientist typically performs are repetitive and time-consuming.
Therefore, automating some of that repetitive work will help free up both new and experienced
data scientists to do the important work that they are trained to do.
The AutoAI system was developed by IBM Research experts in collaboration with IBM Distinguished
Engineer and two-time Kaggle Grandmaster Jean-Francois Puget.
It provides a graphical interface to create and deploy machine learning models with real
time visualizations.
AutoAI automatically performs typical machine learning steps, such as:
Data preparation Model selection
Feature engineering Hyper-parameter optimization
Users can view the progress on the graphical interface.
This example shows the training of a model to predict whether or not a customer is likely
to buy a tent from an outdoor equipment store.
We start with structured data.
In this historical data, there are four feature, or “predictor,” columns:
GENDER: The customer’s gender AGE: The customer’s age
MARITAL_STATUS: “Married”, “Single”, or “Unspecified”
and PROFESSION: The general category of the customer’s
profession, such “Hospitality” or “Sales”, or simply “Other.”
The model will learn to predict the value for the IS_TENT column; that is, whether or
not the customer bought a tent.
After we choose IS_TENT as the column to predict, AutoAI analyzes the data and determines that
the IS_TENT column contains True/False information, making this data suitable for a binary classification
model.
The default metric for a binary classification is ROC/AUC.
After we click Run experiment, an infographic shows the process of building the pipelines
as the model trains.
Once the pipeline creation is complete, we can view and compare the ranked pipelines
in a leaderboard.
The pipelines for the sample binary classification model are quite uniform because of the underlying
sample data.
To see pipelines in action, re-run the experiment as a regression experiment to predict purchase
amount.
That experiment gives better variation in the resulting pipelines.
After clicking “Pipeline comparison,” we can see how the pipelines differ on various
measures of model quality.
The pipelines can be saved as Machine Learning assets in the Watson Studio project.
Then they can be deployed and tested.
Currently AutoAI is available only for classification and regression models; there is a plan to
add time series model support in the future.
In this unit, you have learned how AutoAI automates typical data science tasks and helps
get better performing data pipelines more quickly, while also simplifying pipeline deployment
into production in Watson Machine Learning.
In the next section, we will discuss Watson OpenScale, which helps to ensure that your
models are fair, explainable, and up to date.