About This Course
<div>This course is for DevOps practitioners who already know the fundamentals and want to see where Google Cloud's AI tooling actually fits into a real pipeline, not a lab exercise, not a toy demo.</div><div><br></div><div>You will not be training models or doing MLOps. Instead, you'll treat Gemini and Vertex AI as tools inside your existing DevOps workflow, the same way you'd treat a linter, a monitoring dashboard, or a deployment gate.</div><div><br></div><div>The course follows one continuous demo app, a minimal Flask service called crash-app, from its first deploy on Cloud Run through a full AI-augmented Cloud Build pipeline. Along the way you'll containerize and deploy on both Cloud Run and GKE, compare deployment strategies, provision infrastructure with Terraform, and then layer AI directly into the pipeline: a Gemini-powered code review gate that runs on every pull request, automated log summarization after deploy, and an incident response flow that turns a crash alert into a Pub/Sub-triggered AI triage message in your team chat.</div><div><br></div><div>By the end, you'll have a working, end-to-end Cloud Build pipeline with seven real stages, deployed against a live GCP project, that you can adapt directly to your own team's workflow.</div><div><br></div><div>What makes this course different</div><div><br></div><div>Most "AI for DevOps" content either stays theoretical or bolts AI on as an afterthought. This course builds one pipeline from the ground up and treats AI as a first-class stage in it, with the same rigor as build, test, and deploy.</div>
What you'll learn:
- Deploy and manage applications on Cloud Run and GKE, and know when to reach for each
- Build a complete CI/CD pipeline on Cloud Build from scratch
- Provision and tear down GCP infrastructure with Terraform
- Apply canary, rolling, and blue-green deployment strategies with real commands
- Set up Cloud Monitoring, Logging, and alert policies for production services
- Integrate the Gemini API directly into a CI/CD pipeline using the google-genai Python SDK
- Build an AI code review gate that reviews pull request diffs before merge
- Automate post-deploy log analysis and summarization with Gemini
- Design an AI-driven incident response flow: alert policy → Pub/Sub → Gemini agent → team notification
- Understand the Google Cloud AI ecosystem (Gemini API, Vertex AI, Agent Builder) and where each tool fits in a DevOps workflow
- Know the data governance distinctions that matter when using AI tools on production code (billed project vs. free tier)