This advanced course guides you through the complete lifecycle of fine-tuning open-source large language models on custom datasets at scale. You'll master data preparation strategies including cleaning, tokenisation, and quality assurance; configure distributed training across multiple GPUs using industry frameworks; implement evaluation metrics and monitoring; and deploy optimised models using quantisation and inference engines. Designed for ML engineers and MLOps practitioners, this course covers production patterns, error handling, architectural trade-offs, and real-world deployment scenarios. You'll work with tools like Hugging Face Transformers, LoRA adapters, FSDP, and vLLM to build systems that perform reliably in production environments. By completing this tutorial, you'll be equipped to fine-tune models at scale, optimise for cost and latency, and deploy to cloud infrastructure with confidence.

Lessons

  1. Lesson 1: Enterprise Data Pipelines for LLM Training — Building robust data preparation systems with validation and quality gates (+150 XP)
  2. Lesson 2: Fine-Tuning Strategies and LoRA at Scale — Choosing and implementing optimal adaptation techniques (+150 XP)
  3. Lesson 3: Distributed Training Infrastructure — Scaling training across multiple GPUs and nodes (+160 XP)
  4. Lesson 4: GPU Optimisation and Memory Management — Maximising throughput and minimising resource waste (+160 XP)
  5. Lesson 5: Evaluation Frameworks and Model Assessment — Comprehensive metrics and quality assurance protocols (+150 XP)
  6. Lesson 6: Quantisation for Production Inference — Reducing model size and latency without sacrificing quality (+160 XP)
  7. Lesson 7: Inference Optimisation and Deployment Architecture — Building fast, scalable, and cost-effective inference systems (+160 XP)
  8. Lesson 8: Production Operations and Continuous Improvement — Monitoring, observability, and safe model deployment (+160 XP)