Hello. In Azure
Machine Learning, you run workloads
as experiments, but leverage data assets
and compute resources. In an enterprise data
science process, you'll generally want to separate the overall
process into individual tasks and orchestrate these tasks as Pipelines
of connected steps. Pipelines are key to implementing an effective
machine learning operationalization or
MLOps solution in Azure. You'll explore how to define
and run them in this module. It is important to note that the term pipeline is used extensively in machine learning, often with different meanings. For example, in Scikit-learn, you can define
pipelines that combine data pre-processing
transformations with a training algorithm, and in Azure DevOps, you can define a build or
release pipeline to perform the build and
configuration tasks required to deliver software. The focus of this module is on Azure Machine
Learning pipelines, which encapsulates steps that can be run as an experiment. However, it is
important to bear in mind that it's
perfectly feasible to have an Azure DevOps
pipeline with a task that initiates an Azure
Machine Learning pipeline, which in turn includes
a step that trains a model that is based on
a Scikit-learn pipeline. In this lesson, you
will learn how to create an Azure Machine
Learning pipeline, publish in Azure Machine
Learning pipeline, and schedule an Azure
Machine Learning pipeline.