As the adoption of machine learning (ML) grows across industries, managing ML workflows efficiently becomes crucial. Google Cloud MLOps offers robust solutions for continuous delivery and automation pipelines in machine learning, addressing common challenges in the deployment and operationalization of ML models. This article explores these concepts, outlines problems, provides solutions, and presents use cases to illustrate their application.

Introduction to MLOps

MLOps, short for Machine Learning Operations, is a set of practices aimed at automating and enhancing the process of deploying and maintaining ML models in production. It draws inspiration from DevOps principles, focusing on collaboration between data scientists, ML engineers, and operations teams to streamline ML lifecycle management.