Large Language Models: Optimization, Alignment, and Deployment
In this course, learners will explore the latest methods for optimizing, aligning, and deploying large language models. It starts with performance enhancements—FlashAttention, paged‑attention kernels, KV‑cache reuse, and distributed compute. Next, learners will dive into reinforcement‑learning‑based alignment (RLHF, DPO, PPO, GRPO), advanced prompt‑engineering techniques (chain‑of‑thought, test‑time compute scaling retrieval‑augmented generation) to boost practical application and model output quality. Finally, the course covers some latest innovations, including multi-modal language models and agentic AI. Familiarity with Python programming, basic understanding of machine learning and NLP concepts or completion of courses 2-4 of this Large Language Models course series will be highly beneficial. Experience with Jupyter Notebooks and basic PyTorch usage is recommended.
What you will learn:
- Introduction to training and inference optimizations, including FlashAttention, paged‑attention, KV‑cache, and tensor/data parallelism
- Extensions to training with reinforcement learning and the alignment domain, along with the various alignment techniques (RLHF, DPO, GRPO, etc.)
- Introduction to various test time techniques for practical applications, including chain‑of‑thought, function calling, test‑time compute scaling, retrieval‑augmented generation
- Design and code a minimal GRPO alignment loop, compare it with classic alignment, and choose the right algorithm for different alignment goals
- Introduction to LLM domains going beyond language intelligence: Multi-modal models, Agentic AI.
This course is part of the following course program:
Large Language Models Demystified
Instructors
Sai Chand Boyapati
Mr. Boyapati is an internationally recognized expert in software quality assurance (QA), whose groundbreaking work has had a transformative impact on industries worldwide. His influence and contribution in testing span major developments in software products that have redefined their markets.
Mr. Boyapati holds a critical leadership role as Director of Software Quality Assurance in a globally distinguished organization.
In addition to his technical achievements, Mr. Boyapati has served as a peer reviewer and judge in authoritative capacities. He has evaluated numerous research papers for prestigious conferences and hackathons on AI & LLM’s.
He has written extensively on QA, cybersecurity, and artificial intelligence/LLM’s, with articles published in Media. His book, Focus on QA: Redefining Software Testing in the AI-Driven Era, became a bestseller upon release, providing invaluable insights into applying AI to QA processes
Hamza Mohammed, Course Editor
Hamza Mohammed is a Machine Learning Engineer with Samsung Research America. He is an industry expert in deep learning and reinforcement learning, specializing in large language models. Mr. Hamza has a proven research and industry track record applying, optimizing, and accelerating, deep learning and reinforcement learning in various disciplines, including computer vision, natural language processing (including multi-modal modeling), robotics and automation, software engineering and testing, autonomous navigation and ADAS, digital twin simulation, and biomedical imaging. He has designed and optimized ML models and algorithms for edge-compute deployments and is an authority in securing and optimizing AI application security for on-device and on-premise environments. He is a contributor to several open-source projects, an author and peer-reviewer of multiple publications in top-tier ML venues, and is an inventor on key patents. Mr. Hamza holds a B.S. in Electrical Engineering and Computer Sciences, University of California, Berkeley, USA.
Who Should Attend:
AI Researchers and Academics, Data Scientists, Educators and Instructors, Enthusiasts with a Strong Programming Background , Machine Learning Engineers, Software Developers and Programmers, Students (experience in linear algebra and calculus required), Technical Professionals in industry, Technical Product Manager, Technical Leaders
Prerequisites: Basic programming knowledge in Python; no prior experience with language models is required
Course Level: Intermediate
Publication Year: 2026
ISBN: 978-1-7281-7897-4