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⇱ Getting Started with AWS Generative AI for Developers | Coursera


Getting Started with AWS Generative AI for Developers

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Getting Started with AWS Generative AI for Developers

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Gain insight into a topic and learn the fundamentals.
4.6

57 reviews

Beginner level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
4.6

57 reviews

Beginner level

Recommended experience

9 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Amazon Bedrock

  • Foundation Model Selection

  • Responsible AI Implementation

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

Build your Cloud Computing expertise

This course is part of the AWS Generative AI and AI Agents with Amazon Bedrock Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate from Amazon Web Services

There are 2 modules in this course

Get introduced to generative AI with this foundational course designed for developers looking to use AWS' generative AI services. This course serves as your gateway to understanding and implementing generative AI solutions using Amazon Bedrock.

You'll begin by exploring the fundamentals of generative AI, understanding its place within the broader AI landscape, and learning key concepts such as foundation models, prompts, and inference. Through hands-on labs and demos, you'll gain practical experience invoking foundation models and interpreting their responses. The course then dives into Amazon Bedrock Runtime APIs, covering operations like InvokeModel and asynchronous invocations. You'll learn to implement streaming responses, manage provisioned throughput, and apply guardrails to ensure responsible AI use. A significant portion of the course focuses on working effectively with foundation models. You'll explore model selection criteria, learn the art of prompt engineering, and understand how to optimize your interactions with generative AI tools. By the end of this course, you'll have an understanding of generative AI concepts and hands-on experience with Amazon Bedrock. You'll be ready to start integrating AI capabilities into your applications, setting the stage for more generative AI development in subsequent courses. Please note: The hands-on exercises are optional and require access to your own AWS account. Completing these activities may result in minimal usage charges.

In this module, you will gain foundational knowledge of generative AI, including the differences between AI, machine learning, deep learning, and the role of large language and foundation models. You will explore key concepts such as prompts, inference, and responses, and see how these are practically applied using Amazon Bedrock. Through demos and hands-on labs, you will invoke foundation models to generate outputs like text, images, and code. This module also introduces Amazon Q Developer and how generative AI can enhance the software development lifecycle.

What's included

6 videos5 readings1 assignment

6 videosβ€’Total 40 minutes
  • Introduction to the Courseβ€’3 minutes
  • Amazon Bedrock for Generative AIβ€’6 minutes
  • Invoking an Amazon Bedrock Foundation Modelβ€’8 minutes
  • Amazon Q Developerβ€’8 minutes
  • Demo Amazon Q Developer CLI Vibe Codingβ€’6 minutes
  • Demo Amazon Q Developer on Githubβ€’8 minutes
5 readingsβ€’Total 157 minutes
  • Course Roadmapβ€’2 minutes
  • Amazon Bedrockβ€’15 minutes
  • Exercise: Invoking an Amazon Bedrock Foundation Modelβ€’60 minutes
  • Amazon Q Developerβ€’20 minutes
  • Exercise: Debug and Generate Code with Amazon Q Developerβ€’60 minutes
1 assignmentβ€’Total 25 minutes
  • Module 1 Quizβ€’25 minutes

This module focuses on integrating generative AI into applications using Amazon Bedrock’s APIs and runtime features. You will learn to invoke foundation models using synchronous, asynchronous, and batch methods, understanding important configurations like temperature and throughput options. The module also covers choosing the right foundation model, applying prompt engineering techniques, and implementing responsible AI practices such as guardrails. You will also explore real-world developer tasks enhanced by Amazon Q Developer, such as infrastructure generation, automated documentation, and feature development.

What's included

10 videos7 readings2 assignments1 plugin

10 videosβ€’Total 77 minutes
  • Accessing Amazon Bedrock Runtime APIsβ€’9 minutes
  • Asynchronous and Batch Inference β€’11 minutes
  • Demo Amazon Bedrock Samples Repositoryβ€’2 minutes
  • Adding Guardrails for inputs and responsesβ€’8 minutes
  • Choosing a Foundation Modelβ€’6 minutes
  • Prompt Engineeringβ€’4 minutes
  • Demo Amazon Q Developer Dev Agentβ€’8 minutes
  • Demo Amazon Q Developer Dev Agent Feature Devβ€’7 minutes
  • Demo Amazon Q Developer Documentation Agentβ€’4 minutes
  • Tech Talk: What's possible with Generative AI?β€’18 minutes
7 readingsβ€’Total 151 minutes
  • Amazon Q Developer Bedrock APIsβ€’20 minutes
  • Exercise: Amazon Bedrock Guardrailsβ€’60 minutes
  • Responsible AIβ€’20 minutes
  • Foundation Modelsβ€’20 minutes
  • Prompt Engineering Guideβ€’15 minutes
  • Glossary Course 1β€’15 minutes
  • Post-Course Surveyβ€’1 minute
2 assignmentsβ€’Total 85 minutes
  • Module 2 Quizβ€’25 minutes
  • Final Assessmentβ€’60 minutes
1 pluginβ€’Total 15 minutes
  • Post-Course Surveyβ€’15 minutes

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Instructors

Instructor ratings
4.9 (14 ratings)
Amazon Web Services
21 Coursesβ€’139,316 learners

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Frequently asked questions

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When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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