AI System Design

What you’ll learn and how you can apply it

• Apply a repeatable framework for designing production-grade AI systems
• You want a repeatable framework for making architecture, data, and tech-stack decisions for your AI products
• You've shipped an AI prototype but aren't sure how to take it to production with confidence
• Design AI systems tailored to your specific business and technical requirements
• Select the right architecture, retrieval strategy, and technology stack for your use case
• Define evaluation metrics and guardrails to measure the performance of your AI systems

Course description

Writing code is no longer the hard part; AI can do that. Now the most valuable skill an engineer can have is knowing what to build. Most AI systems never make it to production because of bad decisions made earlier in the process. The pressure to ship fast, the hype around AI, and the lack of a structured approach all push engineers toward building before they’ve really thought through what they’re building and why.

In this course, Apoorva Joshi presents you with a structured framework for making the decisions that get AI systems to production. You’ll discover how to identify the business problem, define success metrics, select the right architecture, define guardrails and evaluation metrics, and know what to optimize for when you ship. During the session, you’ll apply this framework to a real-world problem and leave with a concrete system design and a repeatable process for taking AI applications from problem to production.