Description
This sample project demonstrates the end-to-end workflow of training and persisting ML models in watsonx.ai and batch score with Spark at scale in watsonx.data. Use a pipeline to automate the end-to-end AI lifecycle, from loading data, training models, selecting the best result, and persisting the best model to a model repository. 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.
Click Create project to get started or Log In. Don't have an account yet? Sign Up for a free account.