Summary
In this chapter, you have learned how to use and configure Azure Machine Learning pipelines to split an ML workflow into multiple steps, and how to use pipelines and pipeline steps for estimators, Python execution, and parallel execution. You configured pipeline inputs and outputs using Dataset and PipelineData and managed to control the execution flow of a pipeline.
As another milestone, you deployed the pipeline as a PublishedPipeline instance to an HTTP endpoint. This lets you configure and trigger pipeline execution with a simple HTTP call. After that, you implemented automatic scheduling based on time frequency, and you used reactive scheduling based on changes in the underlying dataset. Now the pipeline can rerun your workflow when the input data changes without any manual interaction.
Finally, we also modularized and versioned a pipeline step, so it can be reused in other projects. We used InputPortDef and OutputPortDef to create virtual bindings for data sources...
