AWS Certified Machine Learning Engineer - Associate Certification Logo

AWS Certified Machine Learning Engineer - Associate Certification

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This certification validates your ability to build, train, tune, and deploy machine learning models on AWS. It demonstrates expertise in applying ML techniques using AWS services, making it ideal for professionals aiming to advance their careers in AI and cloud-based machine learning.

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What is AWS Certified Machine Learning Engineer - Associate Certification Training about?

The AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam validates a candidate’s ability to build, operationalize, deploy, and maintain machine learning (ML) solutions and pipelines by using the AWS Cloud. 

What are the objectives of AWS Certified Machine Learning Engineer - Associate Certification Training ?

The exam also validates a candidate’s ability to complete the following tasks: 

  • Ingest, transform, validate, and prepare data for ML modeling. 
  • Select general modeling approaches, train models, tune hyperparameters, analyze model performance, and manage model versions. 
  • Choose deployment infrastructure and endpoints, provision compute resources, and configure auto scaling based on requirements. 
  • Set up continuous integration and continuous delivery (CI/CD) pipelines to automate orchestration of ML workflows. 
  • Monitor models, data, and infrastructure to detect issues. 
  • Secure ML systems and resources through access controls, compliance features, and best practices.

What are the prerequisites for AWS Certified Machine Learning Engineer - Associate Certification Training?

The target candidate should have the following general IT knowledge: 

  • 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 CI/CD pipelines and infrastructure as code (IaC) 
  • Experience with code repositories for version control and CI/CD pipelines 


Recommended AWS knowledge 

  • The target candidate should have the following AWS knowledge: 
  • Knowledge of SageMaker 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
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AWS Certified Machine Learning Engineer - Associate Certification Training - Certification & Exam


Domain 1: Data Preparation for Machine Learning (ML) 

Ingest and store data. 

Transform data and perform feature engineering. 

Ensure data integrity and prepare data for modeling. 


Domain 2: ML Model Development 

Choose a modeling approach. 

Train and refine models. 

Analyze model performance. 


Domain 3: Deployment and Orchestration of ML Workflows 

Select deployment infrastructure based on existing architecture and requirements. 

Create and script infrastructure based on existing architecture and requirements. 

Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines. 


Domain 4: ML Solution Monitoring, Maintenance, and Security 

Monitor model inference. 

Monitor and optimize infrastructure and costs. 

Secure AWS resources.