Open Generative AI: Building and Deploying Open Models

Proprietary AI APIs can be expensive and restrictive, driving developers to seek customizable, self-hosted alternatives. Open generative AI models offer the flexibility to build, fine-tune, and deploy powerful applications on your own terms. This written course guides you through the process of working with open-source AI models, showing you how to adapt them to specific tasks and run them efficiently. What you'll learn: - Understand the fundamental terminology, architecture, and capabilities of open-source text and image models. - Configure and run open models locally or in cloud environments using modern open-source runtimes. - Apply parameter-efficient fine-tuning techniques to adapt pre-trained models to custom datasets. - Integrate open models with external data sources using retrieval-augmented generation patterns. - Optimize model performance and memory usage using quantization and modern deployment frameworks. We begin with the foundational concepts of generative AI and open-source licensing before moving step-by-step through model selection, customization, and deployment strategies. Each module combines clear written explanations with practical, readable code snippets to reinforce your learning. This text-only course is designed for software developers, technical enthusiasts, and beginners looking to explore open-source AI without needing prior experience in machine learning. Start reading today to build your own customized, independent generative AI solutions.