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所在平台: Udemy |
课程主页: https://www.udemy.com/course/amazon-bedrock-with-amazon-q-developer-zero-to-hero-python/
课程评论：没有评论
课程名称：Amazon Bedrock与Amazon Q开发者 - Python从零到英雄 课程概述： 本课程专注于利用Amazon的最新工具——Amazon Bedrock和Amazon Q，掌握生成式人工智能的开发。课程将全面教授如何构建、部署和优化基于AI的应用程序，使用Amazon提供的最先进的AI服务。 您将学到的内容： - 设置和配置Amazon Bedrock以进行AI模型部署 - 集成Claude、Llama 2和Amazon Titan等基础模型 - 利用Amazon Q的AI辅助编程能力进行开发 - 使用AWS AI服务构建生产级应用 - 实施最佳的提示工程和AI安全实践 - 创建可扩展和具有成本效益的AI解决方案 课程内容： 第一部分：开始使用Amazon Bedrock - 介绍Amazon Bedrock架构 - 设置开发环境 - 了解基础模型及其能力 - API集成与身份验证 第二部分：使用基础模型构建 - 文本生成与完成 - 图像生成与处理 - 代码生成与优化 - 针对特定用例的模型微调 第三部分：Amazon Q开发者体验 - AI辅助代码开发 - 代码审查与优化 - 文档生成 - 安全最佳实践实施 第四部分：使用Q为Bedrock编写推理参数代码 - 使用AI助手创建代码 - 开发AI驱动的内容生成器 - 开发图像生成应用 - 实现代码重构系统 第五部分：模型的额外配置 - 系统提示 - 最大长度 - 停止序列 - 防护措施和构建工具 先决条件： - 基本的Python编程知识 - 对AWS服务的熟悉 - 具备适当权限的AWS账户 适合对象： - 希望将AI集成到应用程序中的软件开发人员 - 想要扩展AWS AI专业知识的云工程师 - 评估企业AI解决方案的技术负责人 - 对AI基础设施感兴趣的DevOps工程师
Master Generative AI Development with Amazon Bedrock & Amazon QCourse OverviewDive into the cutting-edge world of generative AI development using Amazon's latest tools - Amazon Bedrock and Amazon Q. This comprehensive course will teach you how to build, deploy, and optimize AI-powered applications using Amazon's most advanced AI services.What You'll LearnSet up and configure Amazon Bedrock for AI model deploymentIntegrate foundation models like Claude, Llama 2, and Amazon TitanDevelop with Amazon Q's AI-assisted coding capabilitiesBuild production-ready applications using AWS AI servicesImplement best practices for prompt engineering and AI safetyCreate scalable and cost-effective AI solutionsCourse ContentSection 1: Getting Started with Amazon BedrockIntroduction to Amazon Bedrock architectureSetting up your development environmentUnderstanding foundation models and their capabilitiesAPI integration and authenticationSection 2: Building with Foundation ModelsText generation and completionImage generation and manipulationCode generation and optimizationFine-tuning models for specific use casesSection 3: Amazon Q Developer ExperienceAI-assisted code developmentCode review and optimizationDocumentation generationSecurity best practices implementationSection 4: Inference Parameters Code with Q for BedrockBuilding a code with AI assistantCreating an AI-powered content generatorDeveloping an image generation applicationImplementing a code refactoring systemSection 5: Additional Configuration for ModelsSystem PromptsMax LengthStop SequenceGuardrails and Builder ToolsPrerequisitesBasic understanding of Python programmingFamiliarity with AWS servicesAWS account with appropriate permissionsWho This Course is ForSoftware developers looking to integrate AI into their applicationsCloud engineers wanting to expand their AWS AI expertiseTechnical leads evaluating AI solutions for their organizationsDevOps engineers interested in AI infrastructure
