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AWS Certified Generative AI Developer Professional (AIP-C01) Bootcamp

Published by O'Reilly Media, Inc.

Intermediate content levelIntermediate

Build and deploy GenAI on AWS

What you’ll learn and how you can apply it

  • Understand the AWS GenAI ecosystem and integration of foundation models
  • Design RAG pipelines, vector stores, and knowledge bases
  • Gain experience building GenAI workflows using Amazon Bedrock and SageMaker
  • Develop skills in prompt engineering, agentic AI, orchestration, and optimization
  • Deploy secure, scalable, and cost-efficient GenAI applications

Course description

Developers who want to demonstrate their commitment to acquiring and enhancing the skills that modern workplaces demand would do well to pursue generative AI developer certification from AWS. This intensive bootcamp equips you with the practical, production-grade skill set needed to thrive in an AI-centric business climate—and to ace the Generative AI Developer – Professional (AIP-C01) exam.

Guided by AI expert Dr. Yasir Khan, you’ll learn how to integrate foundation models, implement retrieval-augmented generation (RAG), manage embeddings and vector stores, and build agentic AI workflows powered by Amazon Bedrock and SageMaker. Join in for real-world demos, hands-on labs, and architectural deep dives that will help you effectively design, build, deploy, and operationalize generative AI applications using AWS.

NOTE: By registering today, you’ll be enrolled for both sessions. While attending individual sessions is an option, we recommend attending both for the full experience.

This live event is for you because...

  • You’re a data scientist who wants to build and evaluate RAG and FM-based applications.
  • You’re an ML engineer who’s looking for hands-on experience on Bedrock, SageMaker, and vector stores.
  • You’re a cloud architect who wants to design secure, scalable GenAI solutions.
  • You’re preparing for the AWS Certified Generative AI Developer – Professional exam and need focused, practical exam-ready training.

Prerequisites

  • Basic knowledge of Python programming and software development
  • An understanding of fundamental AI and machine learning principles
  • Familiarity with libraries, such as NumPy and pandas, and common ML frameworks
  • Basic understanding of cloud computing concepts, with prior exposure to Amazon Web Services

Recommended follow-up:

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Day 1: Foundation Model Integration and Implementation

Introduction (20 minutes)

  • Presentation: Overview of AIP-C01 exam, domains, AWS GenAI services, and bootcamp goals

Designing GenAI solutions and selecting FMs (60 minutes)

  • Presentation: Architectural design and proofs of concepts; FM selection, provider switching, and resilience
  • Demo/lab
  • Q&A
  • Break

Data pipelines and validation for FMs (60 minutes)

  • Presentation: Data validation and processing complex data types; input formatting and data quality enhancement
  • Demo/lab
  • Q&A
  • Break

Vector stores and retrieval mechanisms (50 minutes)

  • Presentation: Vector database architecture and semantic search; metadata frameworks and query handling
  • Demo/lab
  • Q&A
  • Break

Prompt engineering and governance (50 minutes)

  • Presentation: Prompt design and sequential/complex prompts; interactive AI, governance, and quality assurance
  • Demo/lab
  • Q&A

Day 2: Agentic AI, Security, Optimization, and Troubleshooting (4 hours)

Agentic AI and model deployment (60 minutes)

  • Presentation: Autonomous agents, tool integration, and problem-solving systems; FM deployment strategies and large model considerations
  • Demo/lab
  • Q&A
  • Break

Enterprise and API integrations (60 minutes)

  • Presentation: Enterprise architectures and CI/CD pipelines; API integrations and business system enhancements
  • Demo/lab
  • Q&A
  • Break

Responsible AI (50 minutes)

  • Presentation: Safety controls, privacy, and compliance; responsible AI principles and content moderation
  • Demo/lab
  • Q&A
  • Break

Optimization, monitoring and troubleshooting (50 minutes)

  • Presentation: Cost/performance optimization, caching, and throughput; monitoring, evaluation, and troubleshooting FMs and RAG pipelines
  • Demo/lab
  • Q&A
  • Break

Wrap-up and Q&A (20 minutes)

  • Presentation: Exam format, strategies, best practices, certification guidelines, useful links

Your Instructor

  • Yasir Khan

    Dr. Yasir Khan is the founder of 38 Labs, an Enterprise Data & AI consulting group with offices based out of Paris, New York and Bangalore. He holds a PhD in AI and is an instructor at O’Reilly Media mentoring future experts on AI transformation, machine learning, enterprise solutions and digital transformation. Over his career he has published several articles for leading publishing houses in the field of AI. He speaks at several international conferences such as PyCon, PyData, IEEE. In his spare time he likes flying aircrafts, climbing mountains and traveling.

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Skills covered

  • Generative AI
  • AWS Certified Developer - Associate
  • AWS Lambda