Applied Transformers: Debugging and Deploying Language Models

Moving beyond basic API calls is essential for anyone looking to build robust, production-ready AI applications. To truly leverage large language models, you need to understand how they process information under the hood. This text-only course guides you from foundational transformer concepts to practical deployment strategies. You will develop the intuition needed to predict model behavior, diagnose unexpected outputs, and make informed architectural decisions for your AI projects. What you'll learn: - Understand the core mechanics of the self-attention mechanism and transformer architecture. - Analyze model behaviors to effectively debug hallucinations and unexpected outputs. - Evaluate different fine-tuning techniques, including parameter-efficient methods like LoRA. - Configure deployment pipelines considering latency, cost, and model size. - Apply modern evaluation frameworks to measure model performance and safety. You will begin by exploring the foundational terminology and structural concepts of transformers before moving into practical debugging scenarios and deployment workflows. The written explanations and conceptual exercises ensure you build a strong mental model of these complex systems. This course is designed for developers, data enthusiasts, and technical professionals who want to transition from simply calling AI APIs to understanding and engineering transformer-based systems. No advanced machine learning background is required. Start reading today to master the inner workings of modern language models.