This module introduces learners to the foundational concepts of large language model architectures and their practical applications. Learners will explore the core transformer architecture, examining the trade-offs between encoder-only, decoder-only, and encoder-decoder models. They will develop expertise in evaluating model families like GPT, BERT, and T5 against specific business requirements, considering factors such as domain relevance, latency constraints, context length needs, and computational costs. By the end of this module, learners will confidently select and justify the most appropriate LLM architecture for real-world enterprise scenarios.

This module focuses on mastering parameter-efficient fine-tuning techniques to adapt pre-trained LLMs for specialized domains and tasks. Learners will explore advanced methods like LoRA (Low-Rank Adaptation) and other parameter-efficient approaches that dramatically reduce computational requirements while maintaining model performance. Through hands-on experience with industry-standard frameworks like Hugging Face Transformers, learners will master the complete fine-tuning workflow: from data preparation and preprocessing to training configuration, evaluation metrics, and deployment optimization. The module emphasizes practical skills for building domain-adapted models that achieve enterprise-grade performance while balancing accuracy, efficiency, and cost-effectiveness.

This module explores the full deployment pipeline for LLM applications with a focus on scalability, performance, and security. Learners will design serving architectures using APIs and streaming endpoints, integrate enterprise data, and apply retrieval with FAISS. Optimization practices such as caching, load balancing, and autoscaling are introduced to ensure efficiency at scale. Security is emphasized through OWASP guidelines, strong authentication, and defenses against prompt injection attacks. Finally, learners implement monitoring and alerting systems to maintain reliability, compliance, and trust in production environments.