Architecting Generative AI Applications
Build, deploy, and scale production-ready GenAI systems with LLMOps best practices
Created by Leonid Kuligin
Learn how to move generative AI applications from early prototypes to robust, production-ready systems. Explore proven engineering practices for designing, evaluating, and scaling AI solutions that are secure, reliable, and maintainable in real-world environments.
Packt | Mar 2026 | 278 min
What You Will Learn
You will start by scoping real business use cases and aligning them with technical goals. Step by step, you will learn to design core architectures, evaluate models with practical metrics, and implement operational workflows. Along the way, you will apply best practices for deployment, scaling, and reliability to ensure your AI systems succeed in production.
Key Features
- Design and deploy scalable generative AI architectures for real-world use
- Apply LLMOps and SRE best practices to ensure reliability and security
- Evaluate and improve AI systems with robust metrics and A/B testing
Target Audience
Ideal for AI engineers, data scientists, software engineers, and technical leaders ready to move beyond prototypes. If you are looking to deploy, scale, and maintain production-grade generative AI systems, and want to apply durable engineering principles in your work, this course is designed for you.
