AWS Generative AI and AI Agents with Amazon Bedrock Professional Certificate course Syllabus

This professional certificate course is designed for developers to gain hands-on expertise in building scalable Generative AI applications using AWS services, with a focus on Amazon Bedrock. The curriculum spans approximately 13–17 weeks of part-time study and includes foundational concepts, practical implementation, and real-world deployment of AI agents. Learners will progress through structured modules covering core AI principles, AWS integration, prompt engineering, and responsible AI practices, culminating in a final project that demonstrates end-to-end application development.

Module 1: Foundations of Generative AI for Developers

Estimated time: 20 hours

• Understand transformer-based models and large language models (LLMs)
• Learn about tokenization and embeddings in AI models
• Explore model inference basics and architecture principles
• Study real-world developer-focused Generative AI use cases

Module 2: Building Applications with AWS AI Services

Estimated time: 28 hours

• Access foundation models via AWS tools and APIs
• Deploy foundation models using Amazon Bedrock
• Integrate AI capabilities into backend cloud applications
• Design cloud-native architectures for AI-powered applications

Module 3: Prompt Engineering and Advanced Techniques

Estimated time: 22 hours

• Design effective prompts for diverse development scenarios
• Implement retrieval-augmented generation (RAG) pipelines
• Build knowledge-grounded responses using external data sources
• Explore model fine-tuning and customization strategies

Module 4: Deployment, Monitoring, and Responsible AI

Estimated time: 24 hours

• Deploy scalable Generative AI applications on AWS
• Monitor performance, latency, and operational costs
• Apply security, compliance, and governance controls
• Implement responsible AI and ethical best practices

Module 5: Final Project

Estimated time: 30 hours

• Design and build a full-stack Generative AI application
• Integrate AWS AI services and implement RAG pipeline
• Deploy application with monitoring and security controls

Prerequisites

• Familiarity with programming fundamentals (e.g., Python)
• Basic understanding of cloud computing concepts
• Experience with AWS core services (e.g., IAM, Lambda, S3)

What You'll Be Able to Do After

• Explain how Generative AI and LLMs work from a developer's perspective
• Build and deploy scalable AI applications using Amazon Bedrock
• Apply prompt engineering and RAG techniques to improve model accuracy
• Customize and fine-tune foundation models for specific use cases
• Implement secure, monitored, and responsible AI solutions in production