AWS Certified Machine Learning Engineer Associate

The certification covers key stages of the machine learning lifecycle, including data preparation, algorithm selection, and model engineering within AWS services.

Learners explore Amazon SageMaker in depth and gain experience in building, deploying, and managing machine learning models in a cloud environment.

It also introduces the theoretical foundations of machine learning, such as supervised and unsupervised approaches, and helps develop an understanding of how models are created, refined, and applied in practice.

You earn the certification by passing the AWS Certified Machine Learning Engineer Associate examination.

Our exam prep course supports your preparation through a structured combination of in-depth lessons, practical labs, and practice assessments, designed to help you confidently engage with each area of the exam.

This gives you the opportunity to build hands-on experience with AWS machine learning services and strengthen your understanding of the concepts tested in the assessment.

You are not required to take any specific training before taking this course. However, the following pre-requisite knowledge is recommended:

General IT knowledge

Learners are recommended to have the following:

• Suggested 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist. Basic understanding of common ML algorithms and their use cases
• Data engineering fundamentals, including knowledge of common data formats, ingestion, and transformation to work with ML data pipelines
• 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 ML resources
• Experience with continuous integration and continuous delivery (CI/CD) pipelines and infrastructure as code (IaC)
• Experience with code repositories for version control and CI/CD pipelines

Recommended AWS knowledge

Learners are recommended to be able to do the following:

• Suggested 1 year of experience using Amazon SageMaker AI and other AWS services for ML engineering.
• Knowledge of Amazon SageMaker AI capabilities and algorithms for model building and deployment
• Knowledge of AWS data storage and processing services for preparing data for modeling
• Familiarity with deploying applications and infrastructure on AWS
  Knowledge of monitoring tools for logging and troubleshooting ML systems
• Knowledge of AWS services for the automation and orchestration of CI/CD pipelines
• Understanding of AWS security best practices for identity and access management, encryption, and data protection

This certification is suited to professionals who want to work with machine learning solutions in production environments, including those responsible for building, deploying, and operationalising models on AWS. It is ideal for aspiring or practising machine learning engineers who want to deepen their cloud based skills.

It also supports individuals who contribute to solving organisational problems through machine learning and need to develop the engineering capability required to deliver reliable, scalable solutions. 

Achieving the certification demonstrates that you can confidently manage end-to-end machine learning workflows in AWS, and that you are equipped to support your organisation with production-ready solutions. It provides reassurance to employers that you have both technical depth and practical experience.

Our course aligns closely to the certification objectives and supports you in developing the hands-on ability needed to work effectively with modern cloud based machine learning tools.