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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