Conquering the AWS Certified Machine Learning Engineer — Associate: My Roadmap to Success

Author: Anton R. Gordon

Introduction

When I decided to earn the AWS Certified Machine Learning Engineer — Associate credential, I expected a challenging but rewarding journey. And let me tell you — it delivered on both fronts. This exam is geared toward professionals who not only understand machine learning principles but also possess a solid baseline understanding of the AWS ecosystem. If you’re not at the AWS Solutions Architect — Associate level yet, you’ll want to strengthen those foundations first. One great option is taking this Udemy course to build a robust AWS skill set, especially around storage, networking, compute, and security.

Below, I’ll share how I approached this certification — from brushing up on my AWS fundamentals (as we all must do this from time to time) to getting hands-on with SageMaker and Amazon Bedrock. By the end, you’ll have a clear roadmap for tackling the exam.

Breaking Down the Exam

The AWS Certified Machine Learning Engineer — Associate exam is divided into four main domains:

Data Preparation (28%)

• Ingesting, transforming, and validating data for ML workflows.

Model Development (26%)

• Selecting algorithms, training models, and tuning hyperparameters.