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← Back to HubFlagship Masterclass
Applied AI & LLM Systems Engineering
A comprehensive production curriculum covering autonomous agent architecture, fine-tuning methodologies, vector retrieval engines, and scalable inference infrastructure.
Curriculum
Module 1: Large Foundation Models & Pretraining Mechanics
Transformer architectures, attention heads, tokenization limits, and distributed compute clusters.
Module 2: Autonomous Agentic Loops & Deterministic Tool Use
Designing self-healing agent loops, JSON schema function calling, and structured error fallback.
Module 3: Production RAG & Vector Retrieval at Scale
Hybrid search, semantic chunking, re-ranking strategies, and low-latency embeddings cache.
Module 4: Post-Training, SFT & Direct Preference Optimization
Curating instruction datasets, parameter-efficient fine-tuning (LoRA), and alignment evaluations.
"This course demystifies large language model deployment. Dr. Usmani focuses on real production constraints rather than synthetic toy benchmarks."
Duration
18 Modules · Self-Paced