AWS Certified Generative AI Developer
Master building, deploying, securing, and optimizing production-ready generative AI applications on AWS, including foundation models, RAG, and AI agents.
About this course
Take your artificial intelligence development skills to the professional level with this advanced course designed to prepare you for the AWS Certified Generative AI Developer – Professional (AIP-C01) exam. Explore how to design, build, deploy, and maintain production-ready generative AI applications using Amazon Web Services (AWS). Learn how to integrate foundation models and large language models (LLMs) into applications, automate intelligent workflows, and create scalable AI solutions that solve real-world business problems. Discover how to build generative AI applications using Amazon Bedrock, select appropriate foundation models, and implement effective prompt engineering techniques. Explore retrieval-augmented generation (RAG), vector embeddings, semantic search, and knowledge bases to create AI applications that generate accurate, context-aware responses from enterprise data. Learn how to design and implement agentic AI systems that can reason through tasks, use external tools, retrieve information, and interact with business applications. Explore Amazon Bedrock Agents, Amazon Bedrock AgentCore, AWS Lambda, and other AWS services for orchestrating intelligent workflows and integrating AI capabilities into existing systems. Understand the principles of responsible AI, including content safety, fairness, transparency, privacy, and governance. Learn how to protect sensitive information, enforce access controls, secure model interactions, and implement guardrails using Amazon Bedrock Guardrails, AWS Identity and Access Management (IAM), and AWS security services. You'll also explore how to deploy, monitor, and optimize generative AI applications for production environments. Learn techniques for reducing inference latency, managing token consumption, controlling costs, implementing observability, and improving application reliability and scalability. Develop practical knowledge of evaluating foundation models, testing AI-generated responses, identifying hallucinations, troubleshooting application failures, and measuring the quality, safety, and performance of generative AI solutions. Through short, focused lessons, visual architecture diagrams, real-world development scenarios, and interactive challenges, you'll reinforce advanced concepts and build confidence in designing and operating enterprise-grade generative AI applications. Whether you're an experienced software developer, AI engineer, cloud architect, or technical professional looking to specialize in generative AI, this course will help you deepen your AWS AI development expertise and prepare for the AWS Certified Generative AI Developer – Professional certification.
What you will learn
Foundation Model Architecture, Data Processing, and Retrieval
- GenAI Solution Architecture and Proofs of ConceptFree
- Foundation Model Selection, Routing, and LifecycleFree
- Data Validation and Processing Pipelines for FM ConsumptionFree
- Vector Store Architectures and Knowledge Base ManagementFree
- Document Segmentation, Embeddings, and Advanced RetrievalFree
- Prompt Engineering Strategies, Governance, and Prompt FlowsFree
Agentic AI Systems and Application Integration
- Autonomous Agent Architectures and Tool IntegrationsFree
- Model Deployment Strategies and Hosting PatternsFree
- Enterprise Integration, Security Perimeters, and LLMOpsFree
- Real-Time Interaction, Streaming, and FM API IntegrationsFree
- Application Integration Patterns and Developer EnablementFree
AI Safety, Security, and Governance
- Input and Output Safety Controls and Guardrails
- Adversarial Defense, Network Security, and Privacy Controls
- AI Governance, Compliance Mechanisms, and Auditability
- Responsible AI Principles, Transparency, and Fairness
Operational Efficiency and Performance Optimization
- Token Efficiency, Cost Frameworks, and Intelligent Caching
- Application Responsiveness, Latency Optimization, and Workflows
- Throughput Optimization, Model Configuration, and Resource Allocation
- Integrated Observability and Operational Monitoring
Testing, Validation, and Troubleshooting
- Systematic Model Evaluation and Human-in-the-Loop Feedback
- CI/CD Evaluation Gates and Component Quality Testing
- Troubleshooting Ingestion Pipelines and Retrieval Degradation
- Troubleshooting Prompt Engineering and Schema Compliance
- SaaS Data Ingestion and Transformation Pipelines