Local AI Development with Qwen 2.5 and Ollama

Running powerful AI models no longer requires expensive cloud APIs or compromising data privacy. By leveraging local large language models, developers can build secure, highly responsive applications directly on their own hardware. This text-based course guides you through the entire process of setting up and developing applications with Qwen 2.5 and Ollama. You will transition from understanding foundational AI concepts to writing clean Python code that interfaces with local models, handles structured data, and serves predictions through a modern web API. What you'll learn: Understand the fundamentals of local model architecture and quantization; Configure Ollama to run and manage Qwen 2.5 models efficiently on your local machine; Build asynchronous API endpoints using FastAPI to interact with your local models; Implement basic Retrieval-Augmented Generation (RAG) patterns for document querying; Apply prompt engineering techniques to secure structured JSON outputs; Practice writing clean, type-hinted Python code to handle conversational memory. The course begins with essential terminology and setup guides, then moves step-by-step through API integration, memory management, and local backend deployment. You will learn through clear written explanations, structured code walkthroughs, and practical exercises. Designed for beginner software developers and Python enthusiasts, this course requires no prior experience with AI or machine learning. Start building private, powerful AI applications on your local machine today.