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AWS Generative AI Essentials

AWS Generative AI Essentials

位教师：Rafael Lopes

包含在中

了解更多

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

2 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Generate code with Amazon Q Developer
Implement foundation models through Amazon Bedrock
Design effective prompts

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有3个模块

This course introduces developers to Generative AI with AWS from Amazon Q Developer that integrates directly into your development environment to Amazon Bedrock with its foundation models and customization features. You'll learn implementation strategies for integrating these AWS AI services into your development workflow, including working with prompts, setting up guardrails, and implementing best practices for building AI applications. You'll learn about how AWS has transformed the world of sports analytics, marketing platforms, and travel services. Get started and you'll discover how organizations are using AWS' AI services to uplevel their development processes.

单元详情

This foundational module introduces AWS's core AI development tools - Amazon Q, Amazon Bedrock, and Amazon Kiro - teaching students how to effectively leverage these services for practical AI implementation. Students learn essential concepts including prompt engineering techniques like COSTAR framework, non-deterministic systems, and spec-driven development, while gaining hands-on experience with AI-assisted coding, model selection, and workflow automation. Through practical demonstrations and real-world examples, learners will master the skills needed to build AI-powered applications, optimize AI interactions for cost and performance, and integrate generative AI capabilities into their development workflow using AWS's enterprise-grade AI services.

涵盖的内容

11个视频5篇阅读材料1个作业1个应用程序项目

11个视频总计53分钟

Welcome to the Course2分钟
Where The AI Development Journey Begins4分钟
Introduction to Generative AI for Developers7分钟
Amazon Q Developer4分钟
Amazon Q Business3分钟
Amazon Bedrock7分钟
Amazon Bedrock or Amazon SageMaker?3分钟
Prompt Engineering Fundamentals7分钟
Prompt Engineering Fundamentals Demo7分钟
Building Your First AI-Enhanced Application4分钟
Amazon Kiro Hello World6分钟

5篇阅读材料总计16分钟

Welcome to the Course2分钟
AWS Generative AI Services Overview5分钟
Update: Q CLI is now Kiro CLI2分钟
AI Development Best Practices2分钟
Enterprise AI Strategy and Modern Tools5分钟

1个作业总计180分钟

Module Quiz180分钟

1个应用程序项目总计60分钟

Building Text Generation Apps with Amazon Bedrock60分钟

This module explores essential security considerations and best practices for developing AI applications, with a focus on Amazon Bedrock Guardrails and secure coding patterns. Students learn about critical security concepts including prompt injection attacks, content filtering, PII protection, and how to implement robust safeguards using AWS tools like Bedrock Guardrails, Amazon CloudWatch monitoring, and Amazon Q Developer. Through hands-on demonstrations and practical examples, learners gain the skills to identify security vulnerabilities, implement protective measures, and develop AI applications that maintain data privacy while following industry best practices for secure AI development.

涵盖的内容

6个视频5篇阅读材料1个作业1个应用程序项目

6个视频总计40分钟

Building Security into Your AI Applications4分钟
AI Security Fundamentals8分钟
Introduction to Amazon Bedrock Guardrails8分钟
Demo - a Safeguarded AI Application6分钟
GenAI and Security TechTalk7分钟
Securing AI Development with Amazon Q7分钟

5篇阅读材料总计27分钟

AI Governance and Best Practices5分钟
Prompt Injection and Mitigation Strategies10分钟
Code Snippet - Amazon Bedrock API Call With Guardrail in Place2分钟
Using AI to Enhance Cloud Security5分钟
Cloud Security for AI Workloads5分钟

1个作业总计180分钟

Module Quiz180分钟

1个应用程序项目总计60分钟

Building Secure and Responsible Gen AI with GuardRails for Amazon Bedrock60分钟

This advanced module explores cutting-edge AI development techniques and enterprise integration patterns, focusing on Model Context Protocol (MCP) servers, Amazon Kiro's spec-driven development, and AI agent orchestration with knowledge bases. Students learn how to implement real-time data integration through MCP, structure large-scale AI projects using spec-driven methodologies, and build sophisticated AI agents capable of complex decision-making and multi-step task execution. Through hands-on exposure to production deployment strategies and knowledge base implementation, learners gain practical skills for developing scalable, enterprise-grade AI applications while mastering best practices for AI system architecture and integration.