How to Become an AWS Certified Generative AI Developer

You might be asking why you should take these courses before attempting AWS Certified Generative AI Developer – Professional? The simple answer is that these courses will fully prepare you for everything you need to know before you take the AWS Certified Generative AI Developer – Professional certification. AWS Certified Generative AI Developer – Professional is not an easy certification, and prior knowledge of generative AI and data science is essential to acing this certification. 

AWS also recommends 2 or more years of experience building production grade applications on AWS or with open-source technologies, general AI/ML or data engineering experience, and 1 year of hands-on experience implementing generative AI solutions. Securing the certifications mentioned above is a sure-fire way to ensure you have the experience and expertise needed to complete the AWS Certified
Generative AI Developer – Professional certification.

Each of these prior certifications offers different benefits to you as a pursuer of the AWS Certified Generative AI Developer – Professional certification.

• AWS Cloud Practitioner Essentials
  • Understanding the basics of AWS Cloud computing, which is essential for base understanding.
• Agentic AI Foundation
  • Understanding the core principles and strategies for designing agentic AI systems using AWS services, learning how agentic AI differs from traditional conversational systems, and discovering how to use AWS tools to build autonomous, goal-driven solutions that solve real-world problems.
• Machine Learning Engineering on AWS
  • Knowledge of querying and transforming data, knowledge of software
    engineering best practices for modular, reusable code development, deployment, and debugging, familiarity with provisioning and monitoring  cloud and on-premises machine learning resources.
• Practical Data Science with Amazon SageMaker
  • Knowledge on how to solve real-world use cases with machine learning and produce actionable results using AWS tools. The course also teaches practical aspects of model building, training, tuning, and deployment with AWS tools.
• Amazon SageMaker Studio for Data Scientists
  • Helps data scientists prepare, build, train, deploy, and monitor machine learning models quickly by bringing together a broad set of capabilities purpose built for ML. This course prepares experienced data scientists to use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle.

There are optional courses available as well, which are outlined below: