What is a machine learning pipeline?

The core of a machine learning pipeline is to split a complete machine learning task into a multistep workflow. Each step is a manageable component that can be developed, optimized, configured, and automated individually. Steps are connected through well-defined interfaces.

What is a pipeline in Python?

A pipeline ensures that the sequence of operations is defined once and is consistent when used for model evaluation or making predictions. The Python scikit-learn machine learning library provides a machine learning modeling pipeline via the Pipeline class. You can learn more about how to use this Pipeline API in this tutorial:

What is a ML pipeline?

Pipelines have been growing in popularity, and now they are everywhere you turn in data science, ranging from simple data pipelines to complex machine learning pipelines. The overarching purpose of a pipeline is to streamline processes in data analytics and machine learning. Now let’s dive in a little deeper. What is an ML pipeline?

What is machine learning model deployment?

Normally the term Machine Learning Model Deployment is used to describe deployment of the entire Machine Learning Pipeline, in which the model itself is only one component of the Pipeline. As you can see in the above example, this pipeline consists of a Logistic Regression model.