A Roadmap to Learn MLOps: Building and Deploying Machine Learning Models at Scale
MLOps, or machine learning operations, is a set of practices that allow data scientists and engineers to develop, deploy and monitor machine learning models at scale. As a data scientist, learning how to use MLOps can be a valuable skill, and this blog post will provide a roadmap to guide you through the process. Step 1: Understand the basics of MLOps Before diving into using MLOps, it's important to understand the basic concepts of MLOps. This includes understanding the importance of DevOps practices, such as version control and testing, in the machine learning lifecycle. Step 2: Learn to use version control systems Learn how to use version control systems, such as Git, to manage your machine learning code and models. This includes understanding how to use branches, merge and collaborate with other team members. Step 3: Learn to use containerization technologies Learn how to use containerization technologies, such as Docker, to package and deploy your machine learning models in a...