Design Scalable AI Systems and Components
By External Provider
About this course
This intermediate course teaches you how to design scalable, reliable AI systems that work in real-world production environments. You’ll learn how to build end-to-end architectures that meet throughput, latency, and fault-tolerance goals, and you’ll move from conceptual design to detailed component diagrams and interface specifications. Using industry patterns adopted by modern ML teams, you’ll practice estimating QPS, defining autoscaling rules for the inference layer, structuring data flow between the feature store and model API, and instrumenting your system with a monitoring stack. By the end of the course, you will have created a complete architecture document—including diagrams and interface definitions—that engineering teams can use to implement a scalable AI product.
What you will learn
- Design end-to-end AI system architectures that meet throughput, latency, and fault-tolerance goals using industry-standard ML patterns.
- Produce complete architecture documents with component diagrams and interface specifications that engineering teams can implement directly.