Machine Learning Operations (MLOps): Getting Started

Google Cloud introduces MLOps as a way to make model work more reliable and repeatable. Core technologies and CI/CD practices lead into Vertex AI and cloud architecture choices. It is a foundations option for understanding the operational process before concentrating on a specific deployment tool.

What you will learn

• Identify core technologies used in MLOps.
• Connect CI/CD practices with ML systems.
• Plan repeatable training and inference workflows.

Who this suits

Learners beginning MLOps on Google Cloud; preview the technical examples and account requirements before enrolling.

Before you enroll

Check the cloud-account, software and lab requirements before enrolling. Hosting, storage and GPU resources can incur separate charges. Use permitted sample data, record dependencies and test a rollback or failed pipeline step alongside the successful deployment.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Describe a fictional model pipeline and list the information needed to reproduce its data preparation, training configuration and inference environment.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.