Large Language Model (LLM) Engineering: Development, Fine-tuning & Deployment Training Course

Introduction

Large Language Models (LLMs) have fundamentally transformed the landscape of Artificial Intelligence (AI), enabling unprecedented capabilities in natural language understanding and generation. The Large Language Model (LLM) Engineering: Development, Fine-tuning & Deployment Training Course is an intensive program designed for AI practitioners, machine learning engineers, and data scientists eager to master the technical intricacies of building, optimizing, and deploying these powerful models. This course moves beyond theoretical concepts, focusing squarely on the practical engineering skills required to work with state-of-the-art LLMs in real-world applications.

Participants will gain hands-on expertise in critical areas such as selecting appropriate LLM architectures, performing efficient fine-tuning for specific tasks, and ensuring robust deployment strategies. The curriculum covers essential topics like data preparation for LLMs, prompt engineering for optimization, model evaluation, and the vital considerations for scalability, cost-efficiency, and ethical deployment. By the end of this training, you'll be equipped with the advanced skills to develop custom LLM solutions, integrate them into complex systems, and lead the charge in leveraging the full potential of these transformative AI technologies.

Target Audience

• AI/ML Engineers and Researchers focusing on Natural Language Processing (NLP).
• Data Scientists looking to specialize in Large Language Models.
• Software Developers aiming to integrate LLMs into applications.
• DevOps Engineers responsible for deploying and managing AI services.
• Researchers interested in the practical aspects of LLM development.
• Professionals seeking to build and optimize custom LLM solutions for enterprise use.

Duration

10 days

Course Objectives

1. Understand the core architectures and working principles of Large Language Models.
2. Master data preparation and preprocessing techniques specific to LLM training and fine-tuning.
3. Implement strategies for efficient fine-tuning of pre-trained LLMs for various downstream tasks.
4. Learn to effectively evaluate LLM performance using relevant metrics and benchmarks.
5. Develop robust methods for deploying and serving LLMs at scale in production environments.
6. Explore techniques for prompt engineering and optimization to enhance LLM outputs.
7. Address ethical considerations, bias mitigation, and responsible deployment practices for LLMs.
8. Gain practical experience in building end-to-end LLM-powered applications.

Course Content

Module 1. Foundations of Large Language Models

• What are LLMs: Definition, evolution, and significance in AI
• Core architectures: Transformers, attention mechanisms, and their components
• Overview of popular LLMs: GPT-series, BERT, Llama, Falcon, and their characteristics
• Pre-training objectives and techniques for LLMs
• The scaling laws and emergent capabilities of large models

Module 2. Data Preparation for LLMs

• Curating and cleaning large text corpora for pre-training and fine-tuning
• Tokenization techniques: Byte-pair encoding (BPE), WordPiece, SentencePiece
• Data augmentation strategies for text data
• Handling long sequences and context windows
• Preparing datasets for specific LLM tasks (e.g., summarization, Q&A)

Module 3. LLM Fine-tuning Strategies

• Transfer learning principles for LLMs
• Full fine-tuning vs. Parameter-Efficient Fine-Tuning (PEFT) methods (e.g., LoRA, QLoRA)
• Instruction tuning and supervised fine-tuning (SFT)
• Reinforcement Learning from Human Feedback (RLHF) basics
• Selecting appropriate fine-tuning approaches for different use cases

Module 4. Prompt Engineering for LLM Optimization

• Principles of effective prompt design for desired LLM responses
• Zero-shot, few-shot, and chain-of-thought prompting
• Techniques for controlling LLM output: Temperature, top-p, max-tokens
• Iterative prompt refinement and testing
• Designing prompts for specific tasks like summarization, translation, and code generation

Module 5. LLM Evaluation and Benchmarking

• Quantitative metrics for LLM evaluation: BLEU, ROUGE, METEOR for generation
• Perplexity and loss as training metrics
• Human evaluation and qualitative assessment of LLM outputs
• Benchmarking LLMs on common datasets (e.g., GLUE, SuperGLUE, MMLU)
• Identifying and mitigating model biases during evaluation

Module 6. Deployment Strategies for LLMs

• Architecting production-ready LLM inference systems
• API integration for LLMs: REST APIs, gRPC
• Serving LLMs: Model servers, containerization (Docker), orchestration (Kubernetes)
• Optimizing inference speed and memory usage: Quantization, pruning, distillation
• Edge deployment considerations for smaller LLMs

Module 7. Scalability, Cost, and Performance Optimization

• Strategies for cost-effective LLM inference and training
• Distributed training techniques for large models
• Hardware considerations: GPUs, TPUs, and specialized AI accelerators
• Load balancing and auto-scaling for high-traffic LLM applications
• Monitoring LLM performance and resource utilization in production

Module 8. Building LLM-Powered Applications

• Integrating LLMs into various applications: Chatbots, search engines, content platforms
• Retrieval-Augmented Generation (RAG) systems for enhanced knowledge retrieval
• Using LLMs for code generation and understanding
• Building intelligent agents and autonomous workflows with LLMs
• Designing user interfaces for LLM interactions

Module 9. Ethical AI and LLM Governance

• Addressing bias, fairness, and toxicity in LLM outputs
• Data privacy and security considerations for LLM training and deployment
• Responsible development and deployment guidelines for LLMs
• Understanding legal and regulatory implications of LLM use
• Strategies for auditing and governing LLM behavior

Module 10. LLM Frameworks and Libraries

• Overview of popular LLM libraries: Hugging Face Transformers, OpenAI API, LangChain
• Using PyTorch and TensorFlow for custom LLM development
• Tools for LLM fine-tuning and evaluation
• Model hubs and repositories for pre-trained LLMs
• Practical exercises using key LLM development frameworks

Module 11. LLMs for Specific Use Cases

• LLMs in customer service: Automated support, FAQ generation
• LLMs in healthcare: Clinical note summarization, research assistance
• LLMs in finance: Market analysis, report generation
• LLMs in education: Personalized learning, content creation
• Exploring emerging LLM applications across diverse industries

Module 12. Advanced LLM Engineering Topics & Future Trends

• Multi-modal LLMs: Integrating text with images and audio
• Continual learning and adaptation for LLMs
• Agentic LLM systems and autonomous AI
• The future of LLM architectures and capabilities
• Research frontiers and open challenges in LLM engineering

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank