MLOps (Machine Learning Operations) Fundamentals Course Overview

The MLOps (Machine Learning Operations) Fundamentals course is designed to equip learners with the necessary skills to streamline and automate machine learning workflows. This course delves into the integration of machine learning with continuous integration and delivery (CI/CD) pipelines, ensuring models are scalable and maintainable.

Starting with Module 1, learners will explore the common pain points for data scientists and the specific challenges faced in ML engineering. It also sheds light on how Google Cloud facilitates MLOps and the differences between MLOps and traditional manual ML management, including a comparison between DevOps and MLOps.

From understanding Docker containers and Kubernetes in Module 2, to setting up AI Platform Pipelines in Module 3, the course progresses to cover the full spectrum of deploying ML models. Module 4 focuses on model training and serving on AI Platform, while Module 5 and Module 6 dive deeper into using Kubeflow Pipelines and implementing CI/CD best practices within ML systems. The course wraps up with a summary in Module 7, enabling learners to confidently apply MLOps principles to optimize machine learning operations.