Building or scaling Large Language Models (LLMs) requires navigating massive compute costs, complex data curation pipelines, and distributed system architectures. A single unoptimized pre-training run or miscalculated scaling law can result in significant resource waste and exponential API cost overruns. Understanding the exact mechanics of model initialization and optimization is critical for maintaining unit economics in enterprise AI initiatives.
This course provides a comprehensive architectural briefing on the pre-training frameworks that power modern foundation models. Moving beyond abstract concepts, the curriculum examines the precise engineering decisions required to train decoder-only transformers at scale. Learners will explore the mechanics of subword tokenization, the mathematical foundations of cross-entropy loss, and the infrastructure demands of three-dimensional parallelism. By analyzing the intersection of parameters, data, and compute, participants will learn how to architect stable, cost-effective training runs.
