Curriculum Overview: Training and Fine-Tuning Foundation Models (FMs)

This curriculum provides a comprehensive pathway for understanding how to adapt large-scale Foundation Models (FMs) to specific business domains and tasks, specifically focused on the AWS ecosystem.

Prerequisites

Before starting this curriculum, learners should possess the following foundational knowledge:

• Basic AI/ML Concepts: Understanding of supervised vs. unsupervised learning. Example: Knowing that labeling images of cars is supervised, while grouping customer purchase patterns without labels is unsupervised.
• Deep Learning Fundamentals: Familiarity with neural networks and the Transformer architecture.
• Data Literacy: Knowledge of data types (structured vs. unstructured) and data preprocessing steps.
• AWS Cloud Essentials: General understanding of AWS services like Amazon S3 (storage) and basic compute concepts.

Module Breakdown

Learning Objectives per Module

Module 1: FM Pre-training Foundations

• Key Elements: Describe the key elements of training an FM, including pre-training and distillation.
• Self-Supervised Learning: Understand how models learn from massive unlabeled datasets. Example: An FM reading the entirety of Wikipedia to predict the next word in a sentence.

Module 2: Fine-Tuning & Customization

• Methodology: Define methods such as instruction tuning and transfer learning.
• Continuous Pre-training: Explain domain adaptation for evolving data. Example: Updating a medical FM with the latest 2024 clinical research papers to maintain accuracy.
• PEFT: Understand Parameter-Efficient Fine-Tuning (LoRA/ReFT) to save costs.

Module 3: Data Preparation & RLHF

• Data Governance: Identify best practices for data curation and labeling. Example: Anonymizing patient names in a dataset before using it to fine-tune a healthcare chatbot.
• RLHF: Describe Reinforcement Learning from Human Feedback. Example: A human ranking two AI responses for safety, teaching the model to avoid generating harmful content.

Module 4: Evaluation & Performance

• Technical Metrics: Utilize ROUGE, BLEU, and BERTScore for accuracy assessment.
• Human Evaluation: Implement human audits to check for hallucinations.

Visual Anchors

Training vs. Adaptation Pipeline

Weight Adjustment Conceptualization (LoRA)

Success Metrics

To master this curriculum, learners must demonstrate proficiency in:

• Metric Differentiation: Explaining why $ROUGE (Recall-Oriented) is used for summarization while BLEU$ is used for translation.
• Optimization Trade-offs: Selecting between RAG and Fine-tuning based on cost and data freshness.

  [!IMPORTANT] RAG is preferred for dynamic data that changes daily, whereas Fine-tuning is preferred for teaching a model a specific style or technical vocabulary.

• Risk Mitigation: Identifying common pitfalls like model "poisoning" or "jailbreaking."

Real-World Application

Understanding the FM lifecycle is critical for several industry roles:

• Legal Tech: Fine-tuning models on specific case law to extract contract clauses with high precision. Example: A law firm training a model to specifically identify "Force Majeure" clauses in international shipping contracts.
• Healthcare: Using continuous pre-training on medical journals to assist doctors in diagnosis suggestions.
• Customer Service: Implementing RLHF to ensure brand-aligned, polite, and accurate automated support agents.

▶Click to expand: AWS Services for Training

• Amazon Bedrock: Managed service for fine-tuning models like Claude or Llama with a few clicks.
• Amazon SageMaker: For deep customization, distributed training using SMDDP, and managing the full MLOps pipeline.
• Amazon Q: Leveraging fine-tuned models for business-specific coding and task assistance.