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AWS Certified Generative AI Developer Professional Exam Prep
2,466 students
What you'll learn
- Build and orchestrate sophisticated GenAI applications using Amazon Bedrock Agents, Knowledge Bases, and Prompt Flows.
- Leverage Amazon SageMaker JumpStart to deploy, fine-tune, and manage foundation models for your specific use cases.
- Master the end-to-end ML lifecycle, including bias detection and model monitoring using Amazon SageMaker Clarify.
- Integrate pre-trained AI services like Amazon Rekognition, Textract, and Transcribe directly into your applications.
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Course content
13 sections • 151 lectures • 41h 51m total length
Requirements
- An active AWS account is required to perform the hands-on lab exercises.
- A solid understanding of core AWS services (IAM, S3, EC2, Lambda) is assumed.
- Basic familiarity with machine learning concepts (e.g., training vs. inference) is helpful but not strictly required.
- No prior experience with Amazon Bedrock or SageMaker is required; we will start from the basics.
Description
Generative AI is not just a buzzword—it is the biggest shift in software development in decades.
As organizations rush to adopt AI, the demand for developers who can build, secure, and deploy Generative AI applications on AWS has skyrocketed. The AWS Certified Generative AI - Developer certification is the industry's gold standard for validating these cutting-edge skills.
But passing the exam—and actually building these applications—requires more than just reading documentation. You need practical, hands-on experience.
This course is your complete guide to mastering the AWS Generative AI stack.
Designed specifically for developers, this course bridges the gap between theory and real-world implementation. We strip away the complexity and focus on the practical "how-to" of building GenAI solutions.
You will master the following key domains through hands-on labs:
Amazon Bedrock Deep Dive: Go far beyond simple text generation. You will learn to build complex, agentic workflows using Bedrock Agents, implement Retrieval-Augmented Generation (RAG) using Knowledge Bases, and engineer widely effective prompts using Prompt Management and Prompt Flows.
Amazon SageMaker for Developers: You don't need to be a data scientist to use SageMaker. We focus on the developer-centric features, showing you how to use SageMaker JumpStart to deploy and fine-tune foundation models, SageMaker Clarify to detect bias, and SageMaker Data Wrangler to prepare your data efficiently.
MLOps and Governance: Learn how to operationalize your models using SageMaker Model Monitor and Model Registry to ensure your applications remain reliable in production.
AI Services: Integrate powerful, pre-trained AI capabilities into your apps using Amazon Rekognition (computer vision), Amazon Textract (document extraction), and Amazon Transcribe (speech-to-text).
Who is this course for?
Software Developers looking to transition into AI/ML development.
Candidates preparing for the AWS Certified Generative AI - Developer exam.
Solutions Architects designing GenAI applications.
Anyone who wants to move beyond "Hello World" and build enterprise-grade AI solutions.
Don't get left behind. Enroll today and start building the future of software on AWS!
Who this course is for:
- Software Developers who want to build and integrate Generative AI applications on AWS.
- AWS Certification Candidates preparing for the AWS Certified Generative AI - Developer exam.
- Solutions Architects designing GenAI solutions using Amazon Bedrock and SageMaker.
- Machine Learning Engineers looking to leverage managed services like SageMaker JumpStart.
- Technical Leads who need to understand the capabilities of the AWS Generative AI stack.