Generative AI is transforming industries, but most developers struggle to move beyond basic experimentation to production-ready applications. Understanding how LLMs actually work and deploying them effectively requires deep knowledge of architecture, training methods, and optimization techniques.
This course takes you through the complete LLM-based generative AI lifecycle, from data gathering and model selection to performance evaluation and deployment. You’ll master the transformer architecture that powers modern LLMs, understanding not just how these models are trained, but how fine-tuning adapts them to specific use cases. The curriculum covers empirical scaling laws to optimize model performance across dataset size, compute budget, and inference requirements.
What sets this apart is the focus on practical deployment. You’ll apply state-of-the-art training, tuning, and inference methods to maximize performance within real project constraints. Fregly and his team of AWS AI practitioners bring direct experience building and deploying AI in business environments, sharing insights from actual industry implementations.
The course balances technical depth with practical application. You’ll learn prompt engineering techniques, model evaluation strategies, and deployment best practices that enable you to make informed architectural decisions. Industry case studies reveal both challenges and opportunities companies face when implementing generative AI at scale.
This intermediate-level program assumes Python coding experience and familiarity with machine learning fundamentals like supervised learning, loss functions, and data splitting. The hands-on approach builds practical intuition for utilizing this technology effectively, preparing you to create working prototypes and production systems.
