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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-for-presales-and-solutions-architects/
课程评论：没有评论
课程名称：AI在售前与解决方案架构中的应用 概述：欢迎参加《AI在售前与解决方案架构中的应用》课程！本课程针对解决方案架构师、技术负责人以及任何参与设计和实施技术解决方案的人士，帮助他们有效地利用AI。课程内容将为面对客户的解决方案销售专业人员提供AI核心概念、标准AI服务的基础知识，以及将AI融入解决方案设计的实用方法，帮助他们识别机会并与AI/ML团队有效沟通。课程不针对特定供应商，涵盖AWS、GCP和Azure服务以及生成性AI解决方案如ChatGPT、Gemini、Claude和CoPilot。成为客户在AI/ML解决方案方面的可信顾问。 模块1：架构师和工程师的AI基础知识 1.1 引言：为什么AI对解决方案架构师至关重要（5分钟） - AI作为现代解决方案的核心组成部分所带来的变化。 - 对解决方案设计的实际影响。 - 理解AI可以解决的商业问题。 1.2 核心AI概念回顾 - 机器学习（ML）：监督学习、无监督学习、强化学习、神经网络、关键应用及其颠覆性潜力。 - 大型语言模型（LLMs）及其在现代应用中的作用。 1.3 从SA角度看AI/ML项目生命周期 - 确认项目生命周期的各个阶段。 - 问题框架、数据收集与准备、模型训练与评估、部署与MLOps集成。 模块2：AI服务与集成模式 2.1 云AI服务概述 - 管理的AI服务（PaaS/SaaS）： - 视觉：图像识别、物体检测、面部分析 - 语音：语音转文本、文本转语音 - 语言：自然语言处理（NLP）、情感分析、实体提取、翻译 - 生成性AI/LLMs：突出管理的API访问 - 预测/推荐：何时使用管理服务与自定义ML模型。 2.2 常见AI集成模式和数据考量 - 基于API的集成：调用管理的AI服务。 - 异步处理、大批处理、实时推理。 - 数据治理、隐私与安全；AI的数据管道。 该课程旨在为参与技术解决方案设计的团队提供必要的AI知识，使学员能够在AI及机器学习解决方案中提供专业建议。
Welcome to AI for Presales and Solutions ArchitectsTarget Audience: Solutions Architects, Technical Leads, and anyone involved in designing and implementing technical solutions who wants to understand how to leverage AI effectively.This course will help equip customer-facing solutions selling professionals with a foundational understanding of key AI concepts, standard AI services, and practical approaches for integrating AI into solution designs, enabling them to identify opportunities and effectively communicate with AI/ML teams. In this vendor-agnostic course, we will cover AWS, GCP, and Azure services as well as Generative AI solutions such as ChatGPT, Gemini, Claude and CoPilot. Become that Trusted Advisor for your customers in AI/ML solutions.Module 1: AI Fundamentals for Architects and Engineers 1.1 Introduction: Why AI Matters for Solutions Architects (5 minutes)The evolving landscape since AI is now a core component of modern solutions.Practical implications for solution design.Reasoning and understanding business problems that AI could solve.1.2 Core AI Concepts Refresher Machine Learning (ML):Supervised Learning Unsupervised Learning Reinforcement Learning Neural Networks Key applications What it is and its disruptive potential.Large Language Models (LLMs) and Their Role in Modern Applications.1.3 The AI/ML Project Lifecycle from an SA Perspective Identify the phases of the project lifecycle.Problem FramingData Collection & Preparation Model Training & Evaluation Deployment & MLOps Integration Module 2: AI Services & Integration Patterns 2.1 Overview of Cloud AI Services Managed AI Services (PaaS/SaaS):Vision: Image recognition, object detection, facial analysis Speech: Speech-to-text, text-to-speech Language: Natural Language Processing (NLP), sentiment analysis, entity extraction, translation Generative AI/LLMs: Highlighting managed API access Forecasting/Recommendation: When to use Managed Services vs. Custom ML Models 2.2 Common AI Integration Patterns and Data Considerations API-driven Integration: Calling managed AI services.Asynchronous Processing Batch Processing Real-time Inference Data governance, privacy, and security Data pipelines for AI
