Week 5 - AI Platform Integration on AI Platform Engineering Handbook

End-to-End AI Platform Architecture

Mon, 01 Jan 0001 00:00:00 +0000

1. Cloud-Native AI System Architecture

Let’s start from the very beginning. Before cloud existed, if a company wanted to run software, they had to physically buy servers, put them in a room, hire people to maintain them, and pray nothing breaks. If traffic suddenly doubled, you were out of luck — you didn’t have extra hardware sitting around.

Advanced Deployment Strategies

Mon, 01 Jan 0001 00:00:00 +0000

1. Blue-Green Deployment Strategy

Let me start with the problem this solves. You have a model or service running in production serving real users. You’ve built a new version and want to deploy it. The naive approach: shut down the old version, deploy the new one, hope it works. The problem with this: during the switchover, users get errors. And if the new version has a bug, you’ve already killed the old one — rolling back means going through the same painful process again.

Security & Governance

Mon, 01 Jan 0001 00:00:00 +0000

1. DevSecOps Principles

Let’s start with how security used to work, because understanding the old way makes the new way make sense.

Career Positioning & Branding

Mon, 01 Jan 0001 00:00:00 +0000

1. Resume Structuring for AI Infrastructure Roles

Your resume is not a list of things you did. It is a marketing document whose only job is to get you an interview. Every word on it should serve that purpose. AI infrastructure roles — DevOps, MLOps, Platform Engineering, SRE — have specific expectations, and your resume needs to be structured to match those expectations immediately, because the person reading it is spending 15-30 seconds on first pass.