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Machine learning (ML) pipelines are used by data scientists to build, optimize, and manage their machine learning workflows. A typical pipeline involves a sequence of steps that cover the following areas:

  • Data preparation, such as normalizations and transformations

  • Model training, such as hyper parameter tuning and validation

  • Model deployment and evaluation

The Azure Machine Learning SDK for Python can be used to create ML pipelines as well as to submit and track individual pipeline runs.

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