Amazon Bedrock - The Complete Guide to AWS Generative AI

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Bryan Krausen,Alex Horea

7:26:00

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1 - Course Introduction

1 - How to take this course.mp4

02:29

2 - Udemy tips.mp4

03:48

3 - Tools setup.mp4

02:12

2 - Introduction to Amazon Bedrock

4 - Section intro.mp4

00:39

5 - What is Amazon Bedrock.mp4

04:20

6 - Bedrock console overview.mp4

05:54

7 - AWS configure CLI access.mp4

08:55

8 - Bedrock API Boto3 Python.mp4

07:43

8 - Code changes in this lecture.txt

9 - Bedrock API JS SDK TypeScript.mp4

08:32

9 - Code changes in this lecture.txt

10 - Code changes in this lecture.txt

10 - Optional VSCode debug.mp4

06:44

3 - Working with text models

11 - Section intro.mp4

01:02

12 - Amazon Bedrock text models intro.mp4

03:45

13 - Understanding tokens.mp4

06:30

14 - Text models parameters.mp4

05:56

15 - Bedrock text models Python.mp4

06:56

15 - Code changes in this lecture.txt

16 - Bedrock text models TypeScript.mp4

07:46

16 - Code changes in this lecture.txt

17 - Code changes in this lecture.txt

17 - Prompt engineering.mp4

03:48

17 - Prompt engineering docs.txt

18 - Code changes in this lecture.txt

18 - Project ChatBot.mp4

03:34

19 - ChatBot with History Python.mp4

04:01

19 - Code changes in this lecture.txt

20 - ChatBot with History TypeScript.mp4

04:20

20 - Code changes in this lecture.txt

4 - Amazon Bedrock Image models

21 - Section intro.mp4

00:49

22 - Image models intro.mp4

05:03

23 - Stability AI parameters.mp4

03:27

23 - Stability AI params.txt

24 - Code changes in this lecture.txt

24 - Stability AI images Python and TypeScript.mp4

03:10

25 - Code changes in this lecture.txt

25 - Titan model image generation.mp4

03:35

26 - Code changes in this lecture.txt

26 - Titan model image editing.mp4

04:00

5 - Amazon Bedrock embedding models

27 - Section intro.mp4

01:03

28 - Code changes in this lecture.txt

28 - Embeddings and Similarity.mp4

06:58

29 - Code changes in this lecture.txt

29 - Embedding models Python and TypeScript.mp4

03:12

30 - Code changes in this lecture.txt

30 - Text embeddings Python.mp4

04:45

31 - Code changes in this lecture.txt

31 - Text embeddings TypeScript.mp4

04:16

32 - Code changes in this lecture.txt

32 - Image embeddings Python.mp4

05:20

33 - Code changes in this lecture.txt

33 - Image embeddings TypeScript.mp4

06:25

34 - Vector databases.mp4

04:07

6 - Halfway discussion

35 - Section intro.mp4

02:02

7 - Project RAG app local

36 - Section intro.mp4

02:28

36 - langchain-py.zip

36 - langchain-ts.zip

37 - Langchain intro.mp4

02:09

38 - Code changes in this lecture.txt

38 - First Chain Python.mp4

05:40

39 - Code changes in this lecture.txt

39 - First Chain TypeScript.mp4

05:37

40 - What is a RAG app.mp4

03:17

41 - Basic RAG app Python.mp4

07:15

41 - Code changes in this lecture.txt

42 - Basic RAG app TypeScript.mp4

07:21

42 - Code changes in this lecture.txt

43 - Code changes in this lecture.txt

43 - Document app Python.mp4

07:10

44 - Code changes in this lecture.txt

44 - Document app TypeScript.mp4

06:54

8 - Practice Text API

45 - Section intro.mp4

02:08

46 - Project architecture.mp4

03:18

47 - Code changes in this lecture.txt

47 - Summary Lambda Python.mp4

08:42

48 - Code changes in this lecture.txt

48 - Summary Lambda test Python.mp4

05:25

49 - Code changes in this lecture.txt

49 - Summary Lambda TypeScript.mp4

08:56

50 - Code changes in this lecture.txt

50 - Summary Lambda test TypeScript.mp4

06:15

51 - ApiGateway and Lambda integration.mp4

05:04

51 - Code changes in this lecture.txt

52 - Code changes in this lecture.txt

52 - IAC Summary api CDK Python.mp4

09:32

53 - Code changes in this lecture.txt

53 - IAC Summary api CDK TypeScript.mp4

09:10

9 - Practice Image API

54 - Section intro.mp4

02:15

55 - Project architecture.mp4

02:40

56 - Code changes in this lecture.txt

56 - Image Lambda Python.mp4

07:47

57 - Code changes in this lecture.txt

57 - Lambda Test Python.mp4

08:17

58 - Code changes in this lecture.txt

58 - Image Lambda TypeScript.mp4

08:36

59 - Code changes in this lecture.txt

59 - Lambda Test TypeScript.mp4

08:50

60 - ApiGateway and Lambda integration.mp4

06:04

60 - Code changes in this lecture.txt

61 - Code changes in this lecture.txt

61 - IAC Image api CDK Python.mp4

13:54

62 - Code changes in this lecture.txt

62 - IAC Image api CDK TS.mp4

11:42

10 - Practice Bedrock knowledge bases

63 - Section intro.mp4

04:50

64 - What is Bedrock knowledge base.mp4

04:01

65 - Bedrock Knowledge base model access.mp4

02:36

66 - New console account creation.mp4

02:49

67 - Creating a knowledge base.mp4

06:37

68 - Code changes in this lecture.txt

68 - RAG Lambda Python.mp4

08:10

69 - Code changes in this lecture.txt

69 - RAG Lambda TypeScript.mp4

08:40

70 - Code changes in this lecture.txt

70 - RAG Lambda AWS test.mp4

04:43

71 - Code changes in this lecture.txt

71 - RAG ApiGateway integration.mp4

03:28

72 - Resorces deletion.mp4

02:14

11 - Custom Amazon Bedrock models

73 - Section intro.mp4

01:11

74 - Fine tuning.mp4

04:53

75 - Custom models Amazon Bedrock console.mp4

04:45

12 - Ending section

76 - Thank you.mp4

01:32

13 - Optional AWS Recap

77 - Section intro.mp4

01:02

78 - AWS IAM presentation.mp4

11:09

79 - AWS Lambda presentation.mp4

07:39

80 - AWS API Gateway presentation.mp4

05:22

14 - Optional Infrastructure as code

81 - Section intro.mp4

01:54

82 - AWS CloudFormation.mp4

04:48

82 - Code changes in this lecture.txt

83 - AWS CDK install.mp4

02:38

84 - AWS CDK with Python.mp4

08:39

84 - Code changes in this lecture.txt

85 - AWS CDK with TypeScript.mp4

07:06

85 - Code changes in this lecture.txt

86 - Cleanup.mp4

01:42

Description

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)

Who is this for?

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

What You Need to Know?

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

More details

Description
Unleash the Power of Generative AI on AWS with This Comprehensive Course!
Welcome to Amazon Bedrock - The Ultimate Guide to AWS Generative AI â€“ 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 â€“ 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 access
Code examples with Python and TypeScript
Integration between Bedrock and LagChain
Building an Amazon Bedrock chat bot with history
Building Image APIs backed by Amazon Bedrock
Learn all about the essence of AI: embeddings with Bedrock
Build state of the art RAG app with Bedrock Knowledge bases
Fine 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!
Who this course is for:
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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Bryan Krausen is a Senior Technical Consultant with over 20 years in enterprise IT. Currently, Bryan specializes in Amazon Web Services and HashiCorp tools. Bryan focuses on cloud design, facilitating customer workshops that define business and technical requirements to allow businesses to deliver applications on the AWS cloud platform. Bryan is also the first person to earn the HashiCorp Vault Expert partner certification.Bryan has deployed and assisted with the adoption of HashiCorp Vault for many Fortune 1000 companies.Bryan often speaks at community events, such as HashiConf (2019, 2020, 2021, 2022), HashiTalks (2019, 2020), and many HashiCorp User Groups across the mid-west and the eastern US.All course material can be found on GitHub.

Alex Horea

Instructor's Courses

Hi! I'm Alex and I am pleased to meet you!I have a master's degree in Internet Technologies and I'm an active senior web and Cloud developer. In the last years I worked designing and implementing user interfaces and cloud solutions for various clients ranging from automotive to e-commerce. I firmly believe that knowing is never enough. Knowledge must be put to practice, that is why I want to focus my teaching on concrete, real world programs.

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