Distributed AI Systems
A practical guide to building scalable training, inference, and serving systems for production AI
Created by Fuheng Wu
Explore the practical steps needed to build scalable AI systems for training, inference, and deployment. You'll move from hardware planning and resource estimation to orchestrating clusters and serving models in production environments. Gain hands-on experience with real-world distributed AI workflows.
Packt | Jun 2026 | 558 min
What You Will Learn
You'll work through practical examples that guide you from setting up hardware and preparing data to deploying and tuning distributed AI systems. Each section builds on the last, helping you master both the technical details and the bigger picture of production AI workflows.
Key Features
- Estimate hardware and memory needs for large-scale AI training and inference
- Implement distributed training and inference using parallel and sharded techniques
- Deploy and monitor production AI systems with cluster orchestration and observability
Target Audience
This course is ideal for machine learning engineers, AI researchers, and DevOps professionals aiming to scale AI models in production. If you have a basic understanding of Python and PyTorch, you'll be able to follow along. Prior experience with distributed systems is helpful but not required, as key concepts are introduced from the ground up.
