Machine Learning Operations (MLOps): Getting Started introduces the foundational tools, workflows, and operational practices used to manage machine learning systems in production on Google Cloud.
Designed for professionals working with machine learning infrastructure and deployment workflows, this course explores how MLOps supports reliable, scalable, and repeatable ML operations. You’ll examine the technologies and processes used to deploy, evaluate, monitor, and continuously improve machine learning systems in production environments.
This course explores the key fundamentals of MLOps, including:
- Supporting reliable and repeatable training and inference workflows
- Configuring Google Cloud architectures for effective ML operations
- Working with core technologies that enable MLOps environments
- Applying CI/CD practices to machine learning system and deployment workflows
You’ll learn how machine learning engineers and data scientists collaborate to improve deployment velocity, maintain operational rigor, and support ongoing model evaluation in production systems.
By the end of this course, you’ll be able to explain core MLOps concepts, identify key operational technologies, and apply foundational practices for managing ML systems across the model life cycle on Google Cloud.
