Building and Deploying Generative AI Models

Transitioning from experimenting with AI prompts to deploying reliable generative models in production requires a solid foundation in engineering and architecture. This text-based course bridges the gap between basic AI concepts and production-grade deployments. You will understand how to structure generative AI applications, work with modern large language models, and implement robust deployment workflows. By reading through clear, step-by-step explanations and analyzing structured code snippets, you will gain the confidence to build and maintain AI-powered systems. What you will learn: 1. Understand foundational generative AI concepts, model architectures, and key terminology. 2. Design Retrieval-Augmented Generation (RAG) systems to connect models with external data sources. 3. Implement vector databases to store, index, and retrieve semantic information efficiently. 4. Master prompt engineering principles and basic orchestration frameworks to control model outputs. 5. Deploy generative models safely using basic MLOps practices, monitoring, and evaluation techniques. 6. Configure secure API endpoints to integrate generative features into existing software systems. The course starts with essential definitions and foundational AI concepts before moving into structured text tutorials on RAG architecture, vector databases, and deployment pipelines. This course is designed for software developers, data enthusiasts, and beginners looking to transition into generative AI engineering, with no advanced machine learning background required. Start reading today to build and deploy your first production-ready generative AI application.