Overview
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This program equips you with the knowledge and skills a Generative AI Developer needs to know to build reliable enterprise AI solutions on AWS and helps you prepare for the AWS Certified Generative AI Developer - Professional certification exam.
As a generative AI developer, you'll develop skills to build enterprise-scale solutions through this five-part plan. Part 1 focuses on integrating foundation models with Amazon Bedrock, implement architectures with AWS Lambda and AWS Step Functions, manage multimodal data, and apply prompt engineering. Part 2 explores agentic AI, model deployment, enterprise AI enhancements, FM API patterns, and AI-assisted development tools. Part 3 addresses responsible AI using Amazon Bedrock Guardrails, applying AWS seven-layer security, and establishing governance with Amazon SageMaker Model Cards and AWS Glue Data Catalog. Part 4 covers cost optimization and resource efficiency strategies, enhancing performance with pre-computation and retrieval systems, and monitoring AI workloads including throughput and anomaly detection. Part 5 delivers reliable outputs through evaluation frameworks for relevance and accuracy. You'll apply testing methods like A/B testing and multi-model comparison, develop user-centered quality assurance, and use Amazon Bedrock to detect hallucinations.
Syllabus
- Course 1: Gen AI Dev- Analyze Requirements & Design GenAI Solutions
- Course 2: Gen AI Dev- Select and Configure Foundation Models
- Course 3: Gen AI Dev- Implement data validation & processing pipelines
- Course 4: Gen AI Dev- Design and Implement Vector Store Solutions
- Course 5: Gen AI Dev - Design Retrieval Mechanisms for FM Augmentation
- Course 6: Gen AI Dev- Implement Prompt Eng. Strategies & Governance
- Course 7: Lab - Develop RAG Apps with Amazon Bedrock Knowledge Bases
- Course 8: Gen AI Dev- Agentic AI Solutions and Tool Integrations
- Course 9: Gen AI Dev- Model Deployment Strategies
- Course 10: Gen AI Dev- Enterprise Integration Architectures
- Course 11: Gen AI Dev- Foundation Model API Integrations
- Course 12: Gen AI Dev- Implement App Integration Patterns & Dev Tools
- Course 13: Lab - Develop Conversation Pattern with Amazon Bedrock APIs
- Course 14: Gen AI Dev- Safe User Interactions with Gen AI Applications
- Course 15: Gen AI Dev- Implement Data Security and Privacy Controls
- Course 16: Gen AI Dev- Implement AI Gov, Compliance, & Transparency
- Course 17: Lab - Secure & Resp Gen AI w. GuardRails for Amazon Bedrock
- Course 18: Gen AI Dev- Implementing Cost Opt. & Resource Eff Strategies
- Course 19: Gen AI Dev- Optimize Application Performance
- Course 20: Gen AI Dev- Implement Monitoring Systems
- Course 21: Gen AI Dev- Implement Evaluation Systems for Generative AI
- Course 22: Gen AI Dev- Troubleshoot Generative AI Applications
Courses
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In this module, you will learn how to do the following:Design and implement vector store solutions.
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As organizations increasingly deploy retrieval-augmented generation (RAG) systems to unlock the value of their knowledge assets, the demand for skilled professionals who can architect, implement, and optimize these solutions continues to grow. This curriculum prepares you to meet that demand by providing hands-on experience with AWS services and production-ready implementation patterns.In this module, you will learn how to do the following:Design and implement effective retrieval mechanisms
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Welcome to the first part of this learning plan centered around the role and responsibilities of a generative AI professional developer. In this part, you'll learn how to implement enterprise-scale generative AI solutions using AWS services. You'll learn to integrate foundation models with Amazon Bedrock, implement technical architectures using AWS Lambda and Step Functions, manage multimodal data, leverage vector stores for advanced retrieval, and apply effective prompt engineering strategies.
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Deploying generative AI models in production environments requires strategic planning and careful consideration of performance, scalability, and cost requirements. Modern AI applications demand flexible deployment approaches that can handle varying workloads while maintaining reliability and security.In this lesson, you will learn how to do the following:Describe the benefits and use cases for model deployment strategies in generative AI applications
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Welcome to the second part of this learning plan centered around the role and responsibilities of a generative AI professional developer.In part one, you learned how to analyze your generative AI solution requirements, select foundation models (FMs), process and store data for model consumption, and implement model augmentation, and prompt engineering strategies.In this part, you'll learn how to implement agentic AI solutions, perform various model deployment strategies, design enterprise AI enh
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In this module, you will learn how to do the following:Build effective data validation and processing pipelines for foundation models, ensuring optimal AI performance through high-quality inputs.This module focuses on implementing data validation and processing pipelines for foundation models, covering essential techniques to ensure high-quality data inputs for optimal AI model performance. You will gain comprehensive guidance on data quality management, validation workflows, multimodal processi
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In this module, you will learn how to do the following:Implement model evaluation frameworks.Design flexible routing strategies.Build resilient AI systems with AWS services.Foundation models are transforming how organizations build AI applications, but selecting and configuring the right models requires systematic evaluation and resilient architecture design. This module covers how to assess model capabilities, implement flexible routing strategies, and build resilient AI systems that maintain a
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In this module, you will learn how to do the following:Implement enterprise foundation model control systems with Amazon Bedrock that integrate prompt management, guardrails, and governance frameworks.Build prompt engineering systems combining advanced reasoning techniques and continuous improvement methodologies.This module equips you with the skills to design, implement, and govern effective prompt systems for foundation model (FM) interactions using AWS. You'll learn to optimize model outputs
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In this module, you will learn how to do the following:Define concepts of compliance, governance, and security.Locate AWS resources for compliance programs.Use AWS services and tools to implement compliance and governance controls for your generative AI applications.Without proper governance and compliance frameworks, organizations deploying generative AI face significant legal, ethical, and business risks. Comprehensive governance structures help ensure these powerful technologies are deployed
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Enterprise integration architectures provide the foundation for connecting generative AI systems with existing business applications and data sources. In this module, you'll explore how to design and implement integration patterns that connect foundation models (FMs) with enterprise systems while maintaining security, scalability, and performance requirements.In this lesson, you will learn how to do the following:Describe the core principles of enterprise integration architectures for AI systems
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In this module, you will learn how to do the following:Enhance enterprise systems by using AI-assisted AWS servicesImplement AI-powered enhancements for CRM systems, document processing, knowledge management, data processing workflows, and development productivity toolsIn this module, you learn how to implement application integration patterns and AI-assisted development tools for generative AI applications.
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Foundation model (FM) API integrations form the backbone of modern generative AI applications on AWS. When you understand how to implement these integrations effectively, you can build scalable, production-ready applications. These applications can utilize the power of more than 100 serverless FMs supported by Amazon Bedrock and other AWS AI services. In this module, you'll explore the different types of foundation model request patterns, and you'll learn how streaming responses are used to impl
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This module focuses on implementing comprehensive monitoring systems specifically designed for generative AI applications. You will learn to create actionable dashboards, establish performance baselines, detect anomalies, and monitor specialized components like vector databases that are unique to AI workloads.In this lesson, you will learn how to do the following:Implement comprehensive monitoring and observability systems for generative AI applicationsMonitor and optimize vector database perfor
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Welcome to the fifth part of this learning plan centered around the role and responsibilities of a generative AI professional developer.In this part, you will learn critical skills for ensuring reliable generative AI outputs. You'll learn to build evaluation frameworks measuring relevance and accuracy, implement testing methodologies like A/B testing and multi-model comparison, and develop user-centered quality assurance processes. The curriculum covers troubleshooting techniques for AI-specific
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In this module, you will learn how to do the following:Describe defense-in-depth for AI strategy and the seven layer approach.Locate resources for AI security best practices.Implement secure infrastructure for AI data processing with network isolation by using VPC endpoints.Secure data access patterns with IAM policies.Configure identity federation for enterprise AI environments.Design role-based access control (RBAC) for AI resources and data stores.Implement granular data access with AWS Lake
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Welcome to the third part of this learning plan centered around the role and responsibilities of a generative AI professional developer.In part two, you learned how to implement Agentic AI solutions and tools. You also learned about AI agents, how they work, make decisions, and take actions.In this part, you'll learn about security, governance, and compliance when working with your AI applications, and work with AI-assisted development tools.
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Welcome to the fourth part of this learning plan centered around operational excellence for generative AI applications.In this part, you'll learn how to implement cost optimization and resource efficiency strategies, optimize application performance through pre-computation and retrieval systems, and implement comprehensive monitoring systems specifically designed for generative AI workloads. You'll master techniques for throughput optimization, parameter tuning, API profiling, and automated anom
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This module focuses on optimizing the performance of generative AI applications through systematic approaches to pre-computation, retrieval systems, model configuration, and API profiling. You will learn practical techniques to enhance response times, improve user experience, and maximize resource efficiency in production environments.
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In this lesson, you will learn how to do the following:Describe common issues and challenges of generative AI.
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In this lab, you explore how to use the Amazon Nova Lite model through Amazon Bedrock API for intelligent question answering. You first examine the limitations of zero-shot prompting, and then learn how to enhance response accuracy by providing relevant context. This lab demonstrates both complete and streaming response generation methods, simulating a simplified version of Retrieval-Augmented Generation (RAG) for enterprise-level question answering systems.
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This lab provides hands-on training in building a secure generative AI chatbot using Amazon Bedrock. Participants will learn how to leverage Retrieval-Augmented Generation (RAG) to generate contextually relevant responses. They will implement guardrails to filter and control the chatbot's content generation. Additionally, participants will explore essential security features like access control and logging to ensure the Bedrock configuration adheres to security best practices. By completing this lab, participants will acquire skills to develop secure and compliant generative AI applications aligning with organizational requirements and ethical guidelines.
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In this lab, you build a question-answering application using the AnyCompany knowledge base and Amazon Bedrock's Retrieve and RetrieveAndGenerate APIs. You leverage the existing knowledge base, which contains comprehensive information about AnyCompany's products, services, corporate details like history, leadership, financial performance, sustainability efforts, and more. You run through various notebooks that can effectively answer questions related to AnyCompany's products, services, and corporate information.
Taught by
AWS Instructor