Automating Machine Learning Pipelines with CI/CD/CT: A Guide to MLOps Best Practices

MLOps, short for Machine Learning Operations, is an emerging practice that brings together the disciplines of machine learning and DevOps to streamline the entire lifecycle of machine learning models, from development to deployment and beyond. One of the key aspects of MLOps is the use of automation to improve the efficiency, reliability, and quality of machine learning pipelines. In this tutorial, we will explore how to use Continuous Integration (CI), Continuous Delivery (CD), and Continuous Testing (CT) to automate the deployment of machine learning models. Before we dive into the details of MLOps automation, let's briefly explain the three key concepts that underpin it: MLOps automation typically involves a series of steps that automate the entire machine learning pipeline, from data preparation to model deployment. To automate this process, we can use a combination of CI/CD/CT tools and techniques.