Training & Fine-Tuning LLMs for Production
Second course in the Gen AI 360 Foundational Model Certification that teaches how to train, fine-tune, evaluate, and deploy Large Language Models (LLMs) into production using techniques like LLMOps, LoRA, SFT, RLHF, and cloud-based workflows through 50+ lessons and 10 practical projects.
What you'll learn
- Understand the evolution, fundamentals, and types of Large Language Models, including Transformers and GPT
- Learn when and how to train LLMs from scratch versus fine-tuning existing models using LLMOps best practices
- Gain hands-on experience with fine-tuning techniques such as LoRA, SFT, RLHF, and domain-specific customization
- Work through 10 practical projects using tools like Deep Lake, Cohere, and cloud infrastructure
- Explore deployment challenges and techniques including quantization, pruning, and cloud CPU deployment
Skills you'll gain
- Explain what LLMs are, how they evolved, and how modern architectures like Transformers and GPT work
- Identify when to train an LLM from scratch versus fine-tuning or using RAG or deep memory approaches
- Train LLMs from scratch using datasets, Deep Lake dataloaders, and cloud infrastructure
- Fine-tune LLMs with techniques such as LoRA and supervised fine-tuning (SFT) for domains like finance and medicine
- Apply RLHF to improve trained models using human feedback
- Evaluate and benchmark LLM performance and mitigate hallucinations and bias
- Deploy LLMs to production using quantization, pruning, and cloud CPU-based deployment
- Build domain-specific and industry-specific LLM applications and AI products
Prerequisites
- • Intermediate Python knowledge
- • Access to moderate compute resources (e.g., Google Colab or similar cloud resources)
Who this course is for
- → Beginners in AI who want to learn how to train and fine-tune LLMs from scratch
- → Current machine learning engineers
- → Students interested in Generative AI and LLMs
- → Professionals considering a career transition to AI
- → Gen AI professionals, executives, and enthusiasts looking to apply LLMs in organizations
Provider
Activeloop
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