🟦 Generative AI on AWS: Foundation Models, Prompt Engineering, Fine-Tuning, RLHF & Scalable AI Development

This comprehensive course explores the foundations of Generative AI and the technologies behind modern AI systems using AWS tools and services. Designed for learners interested in advanced artificial intelligence concepts, the course provides practical knowledge about foundation models, model training, prompt engineering, fine-tuning, reinforcement learning, and scalable AI development on cloud infrastructure.

Students will learn how foundation models power modern AI applications, how to customize and optimize them, and how cloud technologies enable large-scale AI deployment. By the end of the course, learners will understand the complete lifecycle of building, training, optimizing, and deploying generative AI systems using AWS technologies.

🟨 1. Foundation Models Fundamentals

This section introduces the core concepts behind foundation models and their role in modern generative AI systems.

🟩 1.1 What Are Foundation Models?

Learn what foundation models are and how they serve as the backbone of modern AI applications.

🟩 1.2 How Foundation Models Power Generative AI

Understand how large-scale AI models generate text, content, and intelligent responses.

🟩 1.3 Selecting the Right Foundation Model

Explore how to choose the best model based on business requirements, performance, and use cases.

🟩 1.4 Model Performance & Scalability Considerations

Understand efficiency, scalability, latency, and deployment trade-offs.

🟨 2. Prompt Engineering Essentials

This section focuses on creating effective prompts that improve AI model performance.

🟩 2.1 Fundamentals of Prompt Engineering

Learn how prompts influence AI outputs and response quality.

🟩 2.2 Designing Effective Prompts

Create structured prompts that generate accurate and relevant responses.

🟩 2.3 Advanced Prompting Techniques

Explore advanced methods for controlling model behavior and improving results.

🟩 2.4 Prompt Optimization Strategies

Refine prompts for consistency, reliability, and performance.

🟨 3. Fine-Tuning AI Models

This section explains how AI models can be customized for specific domains and tasks.

🟩 3.1 Introduction to Fine-Tuning

Understand the purpose and benefits of model fine-tuning.

🟩 3.2 Preparing Training Data

Learn how to collect, clean, and organize datasets for model customization.

🟩 3.3 Fine-Tuning Workflows

Explore practical methods for adapting models to specialized use cases.

🟩 3.4 Evaluating Fine-Tuned Models

Measure performance improvements and model effectiveness.

🟨 4. Building and Training Foundation Models

This section covers advanced concepts related to training AI systems from scratch.

🟩 4.1 Pretraining Fundamentals

Learn how large language models are initially trained.

🟩 4.2 Large-Scale Dataset Preparation

Understand how massive datasets are collected and processed.

🟩 4.3 Distributed Model Training

Explore scalable AI training using cloud infrastructure.

🟩 4.4 AWS Infrastructure for Model Training

Learn how AWS services support large-scale AI development.

🟨 5. Reinforcement Learning from Human Feedback (RLHF)

This section introduces one of the most important techniques used in modern AI alignment.

🟩 5.1 Understanding RLHF

Learn the fundamentals of reinforcement learning from human feedback.

🟩 5.2 Human Preference Modeling

Understand how human evaluations improve model behavior.

🟩 5.3 Aligning Models with Human Intent

Train AI systems to generate safer and more useful outputs.

🟩 5.4 Real-World RLHF Applications

Explore how leading AI systems use RLHF to enhance performance.

🟨 6. AWS Generative AI Ecosystem

This section explores AWS services used for generative AI development and deployment.

🟩 6.1 AWS AI Services Overview

Discover AWS tools and platforms for AI development.

🟩 6.2 Building Scalable AI Solutions

Learn how to design production-ready AI systems on AWS.

🟩 6.3 Deployment and Monitoring

Understand model deployment, monitoring, and maintenance practices.

🟩 6.4 Best Practices for Cloud AI Development

Apply industry-standard techniques for reliability and scalability.

🟨 7. Final Learning Outcomes

By the end of this course, learners will understand foundation models, prompt engineering, fine-tuning, model training, RLHF, and scalable AI development using AWS technologies.

Students will be able to evaluate, customize, optimize, and deploy generative AI systems while understanding the complete lifecycle of modern AI model development and cloud-based AI operations.