LLM Fine-Tuning and Adaptation Training Course

LLM Fine-Tuning and Adaptation Training Course

Introduction

LLM Fine-Tuning and Adaptation Training Course provides advanced, hands-on expertise in Large Language Model customization, domain adaptation, generative AI optimization, transformer architectures, and enterprise AI deployment. As organizations increasingly adopt foundation models, AI copilots, retrieval-augmented generation (RAG), and intelligent automation, the ability to fine-tune and adapt LLMs has become a critical capability. This course equips participants with practical skills in supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), LoRA, QLoRA, instruction tuning, reinforcement learning from human feedback (RLHF), prompt optimization, and model alignment.

Participants will learn how to transform general-purpose AI models into specialized solutions for industries such as healthcare, finance, education, government, customer service, cybersecurity, and enterprise knowledge management. Through real-world case studies and practical labs, learners will explore data preparation, model evaluation, bias mitigation, efficient training strategies, LLMOps, AI governance, and scalable deployment of customized AI systems. The course enables professionals to build high-performing, secure, and cost-effective AI solutions aligned with organizational objectives.

Course Duration

5 days

Course Objectives

By the end of this course, participants will be able to:

1. Understand LLM architectures, transformer models, foundation models, and generative AI ecosystems. 
2. Apply LLM fine-tuning techniques for domain-specific AI applications. 
3. Master Supervised Fine-Tuning (SFT) workflows for customized language models. 
4. Implement Parameter-Efficient Fine-Tuning (PEFT) using LoRA and QLoRA approaches. 
5. Develop expertise in instruction tuning and AI alignment methodologies. 
6. Prepare and optimize high-quality training datasets for LLM adaptation. 
7. Apply prompt engineering and prompt optimization strategies alongside fine-tuning. 
8. Evaluate customized models using LLM benchmarking, validation metrics, and performance analysis. 
9. Implement responsible AI practices including fairness, safety, and bias reduction. 
10. Optimize LLM training through GPU acceleration, quantization, and efficient computing techniques. 
11. Deploy customized models using cloud AI platforms, APIs, and enterprise AI infrastructure. 
12. Understand LLMOps lifecycle management, monitoring, and continuous model improvement. 
13. Design industry-focused AI solutions using adapted large language models and emerging AI technologies. 

Target Audience

1. AI Engineers and Machine Learning Developers 
2. Data Scientists and Data Analysts 
3. Generative AI Solution Architects 
4. Software Developers Building AI Applications 
5. NLP Engineers and Researchers 
6. Enterprise AI and Digital Transformation Leaders 
7. Cloud Engineers and MLOps Professionals 
8. Business Analysts and Innovation Managers 

Course Modules

Module 1: Foundations of Large Language Models and Adaptation

• Understanding LLM architectures, transformers, and foundation models
• Exploring pre-training, transfer learning, and model adaptation concepts 
• Introduction to GPT, Llama, Mistral, Claude, and open-source LLM ecosystems 
• Understanding tokenization, embeddings, attention mechanisms, and context windows 
• Case Study: Adapting a general-purpose LLM for enterprise customer support automation 

Module 2: LLM Training Data Preparation and Engineering

• Designing high-quality datasets for LLM fine-tuning 
• Data cleaning, annotation, formatting, and augmentation strategies 
• Creating instruction-following datasets and conversational datasets 
• Managing data quality, privacy, and governance requirements 
• Case Study: Building a domain dataset for a healthcare AI assistant 

Module 3: Supervised Fine-Tuning (SFT) Techniques

• Understanding supervised learning workflows for LLM customization 
• Preparing training pipelines for instruction tuning 
• Fine-tuning open-source LLMs using modern frameworks 
• Managing training parameters, epochs, and optimization strategies 
• Case Study: Fine-tuning an LLM for legal document analysis 

Module 4: Parameter-Efficient Fine-Tuning (PEFT)

• Understanding LoRA, QLoRA, adapters, and lightweight tuning methods 
• Reducing computational costs through efficient fine-tuning 
• Applying quantization techniques for resource optimization 
• Comparing full fine-tuning versus PEFT approaches 
• Case Study: Customizing an LLM on limited GPU resources for a startup AI product 

Module 5: Advanced LLM Adaptation and Alignment

• Implementing instruction tuning and preference optimization 
• Understanding RLHF and human feedback mechanisms 
• Improving model behavior through alignment strategies 
• Reducing hallucinations and improving response accuracy 
• Case Study: Aligning an enterprise chatbot for safe financial advice 

Module 6: LLM Evaluation, Testing, and Optimization

• Designing evaluation frameworks for customized LLMs 
• Measuring accuracy, relevance, safety, and response quality 
• Using benchmarking tools and automated evaluation methods 
• Performing error analysis and continuous improvement 
• Case Study: Evaluating an AI-powered educational tutoring model 

Module 7: Deployment, LLMOps, and Enterprise Integration

• Deploying fine-tuned models using cloud and on-premise infrastructure 
• Integrating customized LLMs through APIs and AI platforms 
• Monitoring model performance and managing lifecycle operations 
• Implementing scalable AI pipelines and production workflows 
• Case Study: Deploying an enterprise knowledge assistant using a fine-tuned LLM 

Module 8: Responsible AI, Security, and Future Trends

• Applying AI governance frameworks for customized models 
• Managing privacy, security, and ethical AI risks 
• Protecting models against prompt attacks and data leakage 
• Exploring future trends in multimodal AI and autonomous agents 
• Case Study: Developing a secure government AI assistant using adapted LLM technology 

Training Methodology

• Interactive lectures and presentations.
• Group discussions and brainstorming sessions.
• Hands-on exercises using real-world datasets.
• Role-playing and scenario-based simulations.
• Analysis of case studies to bridge theory and practice.
• Peer-to-peer learning and networking.
• Expert-led Q&A sessions.
• Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.