Architecting Generative AI Applications
Build, deploy, and scale production-ready GenAI systems with LLMOps best practices
Created by Leonid Kuligin
Learn how to move from AI prototypes to robust, production-ready generative AI systems. Explore proven engineering practices for designing, deploying, and scaling applications that are reliable, secure, and maintainable. Gain practical skills to operationalize AI solutions in real-world environments.
Packt | Mar 2026 | 278 min
What You Will Learn
You will start by exploring core generative AI architectures and progress to hands-on evaluation and deployment strategies. Along the way, you will use practical workflows and best practices for LLMOps, security, and observability. Each step builds your ability to design, launch, and maintain scalable AI applications with confidence.
Key Features
- Design and evaluate generative AI architectures for real-world deployment
- Apply LLMOps and SRE practices to ensure reliability and scalability
- Run A/B tests and use robust metrics to measure and improve system impact
Target Audience
Ideal for AI engineers, data scientists, software engineers, and technical leaders who want to advance beyond prototyping. If you are looking to deploy, scale, and maintain production-grade generative AI systems, or lead teams building these solutions, you will find actionable guidance and practical tools here.
