Train AutoAI and reference model
Tags
Watson Machine Learning
Required Services
1
Modified
Jun 24, 2022

This sample Watson Studio project demonstrates the capabilities of the Watson Studio Pipeline editor. Use a pipeline to automate the end-to-end AI lifecyle, from loading data, training models, selecting the best result, and deploying the best model to a deployment space. You create and configure the flow once, and then run it on demand or on a schedule, without having to interact with individual tools and assets.

In this sample, a pre-populated project is added to Watson Studio for you. Use the sample pipeline to:

  1. Copy sample assets into a space.
  2. Run a notebook and an AutoAI experiment simultaneously, on a common training data set.
  3. Use another notebook to compare the results and select the best model, ranked for accuracy.
  4. Create a web service deployment for the selected model.
  5. Export selected model to a location in the COS.
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