|
Hello Reader, A container icebreaker interview question is the fundamental container workflow from the local machine to the cloud. In this edition, we will start with the manual process that every project starts with, then we will show how this manual process gives way to CICD and then how it evolves to a more mature pipeline. Let's start with understanding the foundational flow first:
This is the manual flow, and most of these steps in real-world projects are automated. Let's take a look at that next on how these manual steps are translated into CICD:
The above is the foundational CICD. The testing, and security checks are eventually included in the CICD pipeline itself. As a final iteration, let's take a look at that: Raj Speaking in AWS Re:Invent 🎤 Next week, I will be at Las Vegas, USA, Dec 2-6. I will speak there at three sessions including one major breakout session. I will also be at AWS Kubernetes Kiosk on the Expo Floor Wednesday from 1:00 pm - 4:00 pm local time if you want to come and say hi 👋! I will post some pics from the event in the next edition's newsletter. If you have found this newsletter helpful, and want to support me 🙏: Checkout my bestselling courses on AWS, System Design, Kubernetes, DevOps, and more: Max discounted links AWS SA Bootcamp with Live Classes, Mock Interviews, Hands-On, Resume Improvement and more: https://www.sabootcamp.com/
Keep learning and keep rocking 🚀, Raj |
Free Cloud Interview Guide to crush your next interview. Plus, real-world answers for cloud interviews, and system design from a top AWS Solutions Architect.
Hello Reader, Every CEO in the last two years stood on a stage or got on an earnings call and said some version of the same thing: "AI is going to transform our operations. We are reducing headcount because AI will handle it." Two stories broke this week that every Solutions Architect and cloud professional needs to understand. Starbucks quietly shut down their AI inventory system after nine months. Deleted the blog post announcing it. No press release. Just an internal memo: go back to...
Hello Reader, Most AI agents built today have a fundamental flaw. They forget everything the moment a session ends. You tell the agent your preferences, your constraints, your context. You close the tab. You come back. It has no idea who you are. This is not a bug. It is the default state of every LLM and agent. They are stateless by design. And if you are building agents or going into SA interviews, understanding how memory works at a system design level is now a baseline expectation. Why...
Hello Reader, GenAI is expensive. Most teams find out how expensive after the bill arrives. The overspend is not random. It comes from the same mistakes made across almost every GenAI project, and most of them are easy to fix once you know where to look. This is a popular interview topic. But when asked "How will you cost optimize Gen AI workflow and application?", some of the average answers I hear is: I will optimize prompts I will use cheaper models I will reduce usage Why are they...