Transform pre-trained language models into domain-specific powerhouses. This advanced course guides you through the complete fine-tuning lifecycle—from data preparation and model selection to optimised inference and production deployment. You'll master three distinct approaches: efficient LoRA adaptation for resource-constrained environments, quantised QLoRA for extreme optimisation, and full fine-tuning for maximum performance when resources permit. Build real-world systems with production-grade patterns including comprehensive error handling, memory management strategies, and architectural decisions that balance performance against computational cost. Learn to navigate common pitfalls: catastrophic forgetting, overfitting on small datasets, inference latency trade-offs, and cost optimisation across training and deployment phases. By completing this course, you'll deploy a fully customised model serving domain-specific tasks at scale, understanding every decision along the way from hyperparameter selection to monitoring in production.

Lessons

  1. Lesson 1: Foundation & Architecture Decisions — Choose your path—LoRA vs QLoRA vs full fine-tuning. Understand trade-offs and production constraints. (+150 XP)
  2. Lesson 2: Data Pipeline & Preparation — Build robust data ingestion, cleaning, tokenisation, and validation. Handle edge cases and scale efficiently. (+150 XP)
  3. Lesson 3: LoRA Fine-Tuning in Depth — Implement parameter-efficient tuning with adapter modules. Optimise memory usage and training speed. (+150 XP)
  4. Lesson 4: QLoRA & Quantised Training — Master quantisation techniques for extreme efficiency. Train 65B+ models on consumer GPUs. (+150 XP)
  5. Lesson 5: Full Fine-Tuning & Advanced Techniques — Implement complete parameter updates, mixed precision training, distributed strategies, and gradient accumulation. (+150 XP)
  6. Lesson 6: Evaluation, Testing & Validation — Measure performance rigorously. Detect catastrophic forgetting, benchmark against baselines, integrate human feedback. (+150 XP)
  7. Lesson 7: Production Deployment & Optimisation — Deploy at scale. Model serving, inference optimisation, monitoring, cost tracking, and error handling patterns. (+150 XP)
  8. Lesson 8: Capstone Project—End-to-End System — Build, fine-tune, evaluate, and deploy a production model. Implement monitoring and continuous improvement. (+200 XP)