Machine Learning with AutoAI

For this part of the workshop, we'll learn how to use AutoAI. AutoAI is a capability that automates various tasks to ease the workflow for data scientists that are creating machine learning models. It automates steps such as preparing your data for modeling, chooses the best algorithm/estimator for your problem, experiments with pipelines and parameters for the trained models.

This section is broken up into the following steps:

• Automate model building with AutoAI

  • 1. Run AutoAI Experiment

  • 2. Save AutoAI Model

  • 3. Promote the model

  • Conclusion

Note: The lab instructions below assume you have completed the pre-work section already, if not, be sure to complete the pre-work first to create a project and a deployment space.

1. Run AutoAI Experiment

• Go the (☰) navigation menu, expand Projects and then click on your analytics project.

(☰) Menu -> your project

• To start the AutoAI experiment, click the Add to project + button from the top of the page and select the AutoAI experiment option.

Adding a project

• Name your AutoAI experiment asset. The Associated Watson Machine Learning Service Instance should already be populated for you. If not, please select the one that you created in pre-work section from the drop down. Then click the Create button.

Naming your experiment

• To configure the experiment, we must first give it the dataset that will be used to train the machine learning model. We will be using one of the CSV file datasets we have preloaded into the project. Click on the Select from project option.

Select data

• In the dialog, select the german_credit_data_noid.csv file and click the Select asset button.

Select data

• Once the dataset is loaded, we will need to indicate what we want the model to predict. Under What do you want to predict? panel, select the Prediction column as Risk.

• AutoAI will set up default values for the experiment based on the dataset and the column selected for the prediction. This includes the type of model to build, the metrics to optimize against, the test/train split, etc. You could view and change these values under Experiment settings, however, for now we will accept the defaults and click the Run experiment button.

Choose Churn column and run

• The AutoAI experiment will now run. AutoAI will run through steps to prepare the dataset, split the dataset into training / evaluation groups and then find the best performing algorithms / estimators for the type of model. It will then build the following series of candidate pipelines for each of the top N performing algorithms (where N is a number chosen in the configuration which defaults to 2):

  • Baseline model (Pipeline 1)

  • Hyperparameter optimization (Pipeline 2)

  • Automated feature engineering (Pipeline 3)

  • Hyperparameter optimization on top of engineered features (Pipeline 4)

• The UI will show progress as different algorithms/evaluators are selected and as different pipelines are created and evaluated. You can view the performance of the pipelines that have completed by expanding each pipeline section in the leaderboard.

autoai progress

• The experiment can take several minutes to complete. Upon completion you will see a message that the pipelines have been created. Do not proceed to the next section until the experiment completes.

2. Save AutoAI Model

• Once the experiment completes, you can explore the various pipelines and options in the UI. Some of the options available are to see a comparison of the pipelines, to change the ranking based on a different performance metric, to see a log of the experiment, or to see the ranked listing of the pipelines (ranking based on the optimization metric in your experiment, in this case, accuracy).

autoai pipelines created

• Scroll down to see the Pipeline leaderboard. The top performing pipeline is in the first rank.

• The next step is to select the model that gives the best result and view its performance. In this case, Pipeline 4 gave the best result for our experiment. You can view the detailed results by clicking the corresponding pipeline name from the leaderboard:

pipeline leaderboard

• The model evaluation page will show metrics for the experiment, confusion matrix, feature transformations that were performed (if any), which features contribute to the model, and more details of the pipeline. Optionally, feel free to click through these views of the pipeline details.

Model evaluation

• In order to deploy this model, click on the Save as button and select the Model option to save it.

Save model

• In the Save as model window you can accept the default values or give your model a meaningful name/description and then click the Save button.

Save model name

• You will receive a notification to indicate that your model is saved to your project. Go back to your project main page by clicking on the project name on the navigator on the top left:

Select Project

• You will see the new model under Models section of the Assets page.

3. Promote the model

• Now that we have saved our model, we can promote it to our deployment space for deployment. Under the Models section of the Assets page, click the name of your saved model:

choose AI model

• Next, click on the Promote to deployment space:

Deploying the model

• Select the deployment space that was created as part of the pre-work as the Target space and click Promote.

Note: This is assuming you have already created a deployment space in the pre-work section of the workshop.

Promote model

• You will see a notification that the model was promoted to the deployment space succesfully.

Create Deployment Space

Congratulation. We have now successfully created a highly optimized machine learning model using AutoAI and prepared it for deployment.

Conclusion

In this section we covered one approach to building machine learning models on Cloud Pak for Data as a Service. We have seen how AutoAI helps to find an optimal model by automating tasks such as:

• Data Wrangling

• Algorithm Evaluation & Selection

• Feature Engineering

• Hyperparameter Optimization