Amazon Bedrock – AWS生成式AI完整指南学习教程 (英文)

Amazon Bedrock – AWS生成式AI完整指南

发布日期：2024年5月
格式：MP4 | 视频：h264, 1920×1080 | 音频：AAC, 44.1 KHz
语言：英语 | 大小：4.14 GB | 时长：7小时26分钟

课程简介：
学习如何使用AWS和Amazon Bedrock部署可扩展、可靠和安全的生成式AI应用程序（Python和TypeScript）。

你将学到：
了解生成式AI的基本原理，包括其应用、算法及其在各行业中的潜在影响。
熟悉AWS生态系统，包括EC2、S3、Lambda等核心服务，部署和管理生成式AI应用所必需的服务。
了解Amazon Bedrock托管服务及其功能和能力。
按步骤指导创建支持生成式AI工作负载的必要基础设施。
使用你喜欢的编程语言（Python或TypeScript）开发基于AWS的生成式AI应用程序。
轻松使用Bedrock知识库开发RAG应用。
将LangChain与Amazon Bedrock集成（Python和TypeScript源代码示例）。
要求：
基本的机器学习和AI知识。
熟练掌握至少一种常用于AI和数据科学的编程语言，如Python。
推荐基本了解AWS服务和基础设施，包括熟悉EC2、S3、Lambda、IAM和VPC等服务。
基本的编程知识——Python或TypeScript。
课程描述：
释放生成式AI在AWS上的力量，欢迎加入《Amazon Bedrock – AWS生成式AI完整指南》，这是你掌握尖端AI技术和Amazon Web Services（AWS）无与伦比的可扩展性的途径。在本课程中，你将深入了解生成式AI，利用其潜力在各个领域创建创新解决方案。无论你是经验丰富的数据科学家、有远见的企业家，还是好奇的开发者，这门课程将助你解锁无限可能。

主要亮点：

实践操作：通过实际练习，使用Python的boto3、JavaScript SDKs和TypeScript，结合VSCode调试，实现无缝开发。
文本和图像模型：探索文本生成的魔力，深入了解最先进的图像生成模型，掌握向量数据库的嵌入技术。
高级应用：从LangChain到RAG应用和文档处理，你将探索广泛的高级应用，帮助你自信地应对复杂挑战。
Amazon Bedrock精通：深入了解Amazon Bedrock，这是在AWS上部署可扩展、可靠和安全生成式AI应用的游戏规则改变者。实践部分确保你熟练掌握Bedrock，准备好应对任何项目。
课程涵盖的关键主题包括：

Amazon Bedrock介绍及控制台和CLI访问设置
Python和TypeScript的源代码示例
Bedrock与LangChain的集成
构建具有历史记录的Amazon Bedrock聊天机器人
构建基于Amazon Bedrock的图像API
了解AI的本质：使用Bedrock进行嵌入
使用Bedrock知识库构建最先进的RAG应用
微调模型并创建自定义模型
为什么选择这门课程？

专家指导：由在AI和AWS领域有多年经验的行业专家授课。
实践方法：通过指导练习和真实案例研究获得实践经验。
不要错过这个机会，成为AI创新世界的先锋！立即报名，开始你的旅程，成为Amazon Bedrock和AWS生成式AI专家。超越理论，从当今编程需求出发，向活跃的讲师学习！

Amazon Bedrock – The Complete Guide To Aws Generative Ai

Published 5/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.14 GB | Duration: 7h 26m

Learn to Deploy Scalable, Reliable, and Secure Generative AI Apps Using AWS and Amazon Bedrock (Python and TypeScript)

What you’ll learn
Understand the fundamentals of Generative AI, including its applications, algorithms, and potential impact across various industries.
Gain familiarity with the AWS ecosystem, including core services such as EC2, S3, Lambda, and more, essential for deploying and managing Generative AI apps
Learn about Amazon Bedrock Managed Service, its features nd capabilities
Step-by-step guidance on creating the necessary infrastructure to support Generative AI workloads.
Develop Generative AI apps backed by AWS with your preferred programming language (Python or TypeScript))
Easily develop RAG apps with Bedrock Knowledge bases
Integrate LangChain with Amazon Bedrock (Python and TypeScript code examples)

Requirements
Basic Knowledge of Machine Learning and AI
Proficiency in at least one programming language commonly used in AI and data science, such as Python
Basic understanding of AWS services and infrastructure is recommended, including familiarity with services like EC2, S3, Lambda, IAM, and VPC.
Basic programming knowledge – Python or TypeScript

Description
Unleash the Power of Generative AI on AWS with This Comprehensive Course!Welcome to Amazon Bedrock – The Ultimate Guide to AWS Generative AI – your gateway to mastering the fusion of cutting-edge AI technology and the unparalleled scalability of Amazon Web Services (AWS).In this course, you’ll dive deep into the world of Generative AI, harnessing its potential to create innovative solutions across diverse domains. Whether you’re a seasoned data scientist, a visionary entrepreneur, or a curious developer, this course is your ticket to unlocking limitless possibilities.Key Highlights:Hands-On Practice: Dive right into real-world scenarios with practical exercises using Python’s boto3, JavaScript SDKs, and TypeScript, coupled with VSCode debugging for seamless development.Text and Image Models: Explore the magic of text generation with chatbots, delve into image generation with state-of-the-art models, and master embedding techniques for vector databases.Advanced Applications: From LangChain to RAG apps and document processing, you’ll explore a wide array of advanced applications, empowering you to tackle complex challenges with confidence.Amazon Bedrock Mastery: Get up close and personal with Amazon Bedrock – the game-changer for deploying scalable, reliable, and secure Generative AI applications on AWS. Practice sections ensure you’re well-versed with Bedrock, ready to tackle any project.Key topics covered in this course include:Amazon Bedrock introduction and setup for console and CLI accessCode examples with Python and TypeScriptIntegration between Bedrock and LagChainBuilding an Amazon Bedrock chat bot with historyBuilding Image APIs backed by Amazon BedrockLearn all about the essence of AI: embeddings with BedrockBuild state of the art RAG app with Bedrock Knowledge basesFine tune models and create your custom models.Why Choose This Course?Expert Guidance: Learn from industry experts with years of experience in AI and AWS.Practical Approach: Gain hands-on experience with guided exercises and real-world case studies.Don’t miss out on this opportunity to become a trailblazer in the world of AI innovation! Enroll now and embark on your journey to becoming a Generative AI expert with Amazon Bedrock and AWS. Go beyond the theory and learn from active instructors, aligned with today’s programming demands!Let’s revolutionize the future together!

Overview
Section 1: Course Introduction

Lecture 1 How to take this course

Lecture 2 Udemy tips

Lecture 3 Tools setup

Section 2: Introduction to Amazon Bedrock

Lecture 4 Section intro

Lecture 5 What is Amazon Bedrock?

Lecture 6 Bedrock console overview

Lecture 7 AWS configure – CLI access

Lecture 8 Bedrock API – Boto3 – Python

Lecture 9 Bedrock API – JS SDK – TypeScript

Lecture 10 Optional – VSCode debug

Section 3: Working with text models

Lecture 11 Section intro

Lecture 12 Amazon Bedrock text models intro

Lecture 13 Understanding tokens

Lecture 14 Text models parameters

Lecture 15 Bedrock text models – Python

Lecture 16 Bedrock text models – TypeScript

Lecture 17 Prompt engineering

Lecture 18 Project: ChatBot

Lecture 19 ChatBot with History – Python

Lecture 20 ChatBot with History – TypeScript

Section 4: Amazon Bedrock Image models

Lecture 21 Section intro

Lecture 22 Image models intro

Lecture 23 Stability AI parameters

Lecture 24 Stability AI images – Python and TypeScript

Lecture 25 Titan model image generation

Lecture 26 Titan model image editing

Section 5: Amazon Bedrock embedding models

Lecture 27 Section intro

Lecture 28 Embeddings and Similarity

Lecture 29 Embedding models – Python and TypeScript

Lecture 30 Text embeddings – Python

Lecture 31 Text embeddings – TypeScript

Lecture 32 Image embeddings – Python

Lecture 33 Image embeddings – TypeScript

Lecture 34 Vector databases

Section 6: Halfway discussion

Lecture 35 Section intro

Section 7: Project RAG app (local)

Lecture 36 Section intro

Lecture 37 Langchain intro

Lecture 38 First Chain – Python

Lecture 39 First Chain – TypeScript

Lecture 40 What is a RAG app?

Lecture 41 Basic RAG app – Python

Lecture 42 Basic RAG app – TypeScript

Lecture 43 Document app – Python

Lecture 44 Document app – TypeScript

Section 8: Practice: Text API

Lecture 45 Section intro

Lecture 46 Project architecture

Lecture 47 Summary Lambda – Python

Lecture 48 Summary Lambda test – Python

Lecture 49 Summary Lambda – TypeScript

Lecture 50 Summary Lambda test – TypeScript

Lecture 51 ApiGateway and Lambda integration

Lecture 52 IAC: Summary api – CDK Python

Lecture 53 IAC: Summary api – CDK TypeScript

Section 9: Practice: Image API

Lecture 54 Section intro

Lecture 55 Project architecture

Lecture 56 Image Lambda – Python

Lecture 57 Lambda Test – Python

Lecture 58 Image Lambda – TypeScript

Lecture 59 Lambda Test – TypeScript

Lecture 60 ApiGateway and Lambda integration

Lecture 61 IAC: Image api – CDK Python

Lecture 62 IAC: Image api – CDK TS

Section 10: Practice: Bedrock knowledge bases

Lecture 63 Section intro

Lecture 64 What is Bedrock knowledge base

Lecture 65 Bedrock Knowledge base model access

Lecture 66 New console account creation

Lecture 67 Creating a knowledge base

Lecture 68 RAG Lambda – Python

Lecture 69 RAG Lambda – TypeScript

Lecture 70 RAG Lambda – AWS test

Lecture 71 RAG ApiGateway integration

Lecture 72 Resorces deletion

Section 11: Custom Amazon Bedrock models

Lecture 73 Section intro

Lecture 74 Fine tuning

Lecture 75 Custom models – Amazon Bedrock console

Section 12: Ending section

Lecture 76 Thank you!

Section 13: Optional: AWS Recap

Lecture 77 Section intro

Lecture 78 AWS IAM presentation

Lecture 79 AWS Lambda presentation

Lecture 80 AWS API Gateway presentation

Section 14: Optional: Infrastructure as code

Lecture 81 Section intro

Lecture 82 AWS CloudFormation

Lecture 83 AWS CDK install

Lecture 84 AWS CDK with Python

Lecture 85 AWS CDK with TypeScript

Lecture 86 Cleanup

Professionals keen on expanding their skill set in AI and machine learning, particularly in the domain of Generative AI,Developers aiming to explore the intersection of AI and cloud computing,Researchers and academics interested in exploring practical applications of Generative AI,IT professionals responsible for managing cloud infrastructure and ensuring its security and scalability

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