Build a Machine Learning Pipeline in Python and Package It as a Docker Container

Learn how to train and develop a machine learning pipeline in Python using PyCaret and then package it using docker container.

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Introduction

A Docker container is a logical separation of the software from the underlying infrastructure. It enables developers to package an application with all of its dependencies and ship it as a single unit. Docker containers have revolutionized the way applications are deployed and run. They provide a consistent and reliable environment for running applications, and they are easy to portable and scalable.

Docker containers are perfect for packaging machine learning pipelines. A machine learning pipeline can be easily containerized with all of its dependencies, and it can be deployed on any cloud infrastructure.

Docker containers provide a great way to package machine learning models for production use. They are easy to deploy and manage, and they offer a consistent and reliable environment for running machine learning models.

In this tutorial we will:

✔️Build end-to-end machine learning pipeline using PyCaret — an open-source, low-code, machine learning…