AI Engineering: Fine-Tuning Open-Source LLMs with QLoRA and AWS

General-purpose artificial intelligence models often lack the specific domain knowledge required for specialized business tasks. To bridge this gap, modern AI engineers must know how to adapt open-source models using efficient, cost-effective customization techniques. This text-based course guides you step-by-step through the process of fine-tuning large language models (LLMs) on your own custom datasets. By completing this course, you will understand the mechanics of model customization, learn how to optimize models with minimal computing resources, and acquire the skills to deploy your models for real-world use. Through detailed written explanations and clear code snippets, you will gain a practical understanding of modern AI deployment workflows. What you'll learn: - Understand the core architecture of LLMs and the fundamentals of fine-tuning. - Apply Parameter-Efficient Fine-Tuning (PEFT) techniques, focusing on LoRA and QLoRA. - Prepare and format high-quality custom datasets for training. - Configure training pipelines using open-source libraries and PyTorch. - Deploy fine-tuned models on AWS SageMaker for scalable inference. - Build interactive user interfaces with Streamlit to showcase your custom LLMs. The curriculum starts with essential terminology, basic concepts, and foundational definitions before moving into dataset preparation, training configurations, and deployment strategies. You will learn by reading comprehensive explanations and studying practical, production-ready code examples. This course is designed for software developers, data practitioners, and technology enthusiasts who want to enter the field of AI engineering. No prior experience with machine learning models or cloud deployment is required, making this the perfect starting point for beginners. Start reading today to unlock the power of custom artificial intelligence.