Whizlabs
Getting Started with Amazon Bedrock
Whizlabs

Getting Started with Amazon Bedrock

Whizlabs Instructor

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Learn how to build and deploy generative AI applications using Amazon Bedrock’s foundation models.

  • Explore no-code prototyping tools like PartyRock to rapidly test AI ideas.

  • Understand how to enhance model performance using Retrieval-Augmented Generation (RAG).

  • Implement Guardrails to ensure responsible AI usage and content safety.

Details to know

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Recently updated!

August 2025

Assessments

6 assignments

Taught in English

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There are 3 modules in this course

This week, we’ll explore the foundational elements of Amazon Bedrock, AWS’s fully managed service for building and scaling generative AI applications with foundation models. You’ll gain a clear understanding of how Bedrock fits into the broader AWS ecosystem and supports serverless, customizable AI solutions. We’ll cover essential topics including the core architecture and pricing model of Bedrock, how to navigate the Bedrock interface, and the use of PartyRock—a no-code playground for quickly prototyping generative AI apps. You’ll also explore responsible AI principles, learn how to evaluate and choose the right foundation models, and see Amazon Bedrock in action through guided demos. By the end of the week, you’ll have a solid understanding of Amazon Bedrock’s capabilities and how to get started with building and experimenting with foundation models in a secure and scalable way.

What's included

8 videos3 readings2 assignments

In Week 2, we’ll shift our focus to Retrieval-Augmented Generation (RAG), safety mechanisms, and agent-based orchestration in Amazon Bedrock. You’ll begin by understanding the architecture and principles behind RAG and how it enhances large language model (LLM) outputs with external knowledge sources. This week also introduces you to Amazon Bedrock Guardrails—a powerful toolset for implementing content safety, privacy filters, and responsible AI controls. You’ll explore how to create and apply Guardrails through practical demos. Finally, you’ll get hands-on with Bedrock Agents, learning how to configure them to automate workflows, enhance interactivity, and support dynamic, multi-step tasks. By the end of this module, you’ll be equipped to build secure, reliable, and intelligent generative AI applications using Amazon Bedrock’s advanced capabilities.

What's included

5 videos1 reading2 assignments

Welcome to Week 3! This final module focuses on advanced topics including workflow automation, system integration, and career development within the Amazon Bedrock ecosystem. You’ll learn how to streamline generative AI processes using Amazon Bedrock Flows and implement data-driven automation through Bedrock Data Automation (BDA). We’ll also explore how to monitor and integrate Bedrock applications with AWS services like Amazon CloudWatch and Amazon S3 to ensure operational visibility and performance. Finally, you’ll gain insight into the certifications, career paths, and job opportunities available for professionals working with generative AI and AWS technologies. By the end of the week, you’ll be equipped with the skills to automate, monitor, and scale generative AI solutions—while confidently navigating your career journey in the AWS ecosystem.

What's included

4 videos1 reading2 assignments1 plugin

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