AWS Certified: Machine Learning Engineer – Associate (MLA-C01)
Intermediate Level
The AWS Certified Machine Learning Engineer – Associate validates your ability to design, deploy, and scale machine learning (ML) solutions on AWS, ensuring robust MLOps practices and operational excellence.
Machine Learning Engineer Associate certification is designed for professionals who build, deploy, and maintain ML solutions on AWS. It targets individuals with at least one year of experience using Amazon SageMaker and other AWS ML services, such as backend developers, DevOps engineers, data engineers, and data scientists.
Build the ability to develop production-ready ML applications on AWS. Learn how to transform your ML expertise into real-world solutions from an expert AWS Instructor
Transform your ML expertise into production-scale solutions on AWS. Learn to build, deploy, and operationalize machine learning applications using Amazon SageMaker and EMR.
Perfect for ML professionals seeking AWS certification, this hands-on course delivers practical skills for implementing enterprise-grade ML solutions in this three-day course.
- Learn to build, deploy, orchestrate, and operationalize ML solutions at scale
- Gain practical experience using AWS services such as Amazon SageMaker AI and analytics tools such as Amazon EMR
- Set up continuous integration and continuous delivery (CI/CD) pipelines to automate orchestration of ML workflows
AWS Certified: Machine Learning Engineer – Associate (MLA-C01)
Category
Associate
Exam Duration:
130 minutes
Exam Format:
65 questions
Cost:
150 USD
Visit Exam pricing for additional cost information, including foreign exchange rates
Language Offered:
English, Japanese, Korean, and Simplified Chinese
Intended candidate:
Individuals with at least 1 year of experience using Amazon SageMaker and other ML engineering AWS services
Candidate role examples:
Backend software developer, DevOps engineer, data engineer, MLOps engineer, and data scientist
Testing Provider:
Pearson VUE testing center or online proctored exam
What are the key skills measured:
- Data Preparation for Machine Learning (ML) – 28%
- ML Solution Monitoring, Maintenance, and Security – 24%
- Deployment and Orchestration of ML Workflows – 22%
- ML Model Development – 26%
Who is this for?
- ML Engineers and Data Scientists building production-ready ML models.
- Solutions Architects designing AWS-based ML systems.
- DevOps Engineers implementing MLOps pipelines.
- IT Professionals transitioning into ML roles.
Showcase proficiency in architecting scalable data models, overseeing end-to-end data lifecycle management, and enforcing robust data quality assurance protocols.
45% Salary Uplift for AI/ML Roles
Professionals who acquire AI and machine‐learning certifications see the largest pay increases of any tech discipline.
Accelerating AI Skills Report
93% of Asia‐Pacific employers expect to use AI tools by 2028, driving urgent demand for certified ML practitioners who can architect, deploy, and scale these solutions on AWS.
Accelerating AI Skills Report
86% of global employers identified AI and information‐processing technologies as a key driver of business transformation, outpacing robotics (58%) and energy tech (41%).
Future Of Jobs Report 2025
Validate ML Expertise:
Demonstrate your ability to build, operationalise, deploy and maintain machine learning solutions and pipelines using AWS. The certification goes beyond ML concepts to assess how models are implemented in production environments.
Build End-to-End ML Capability
Develop skills across data preparation, model development, deployment, workflow orchestration, monitoring, maintenance and security. These four areas make up the core MLA-C01 certification domains.
Strengthen MLOps Expertiset:
Build practical knowledge of deployment infrastructure, automated orchestration and CI/CD pipelines for ML workflows. This makes the certification particularly relevant to professionals moving towards MLOps and production AI responsibilities.
Prepare for In-Demand ML Roles:
Build credibility for technical roles such as ML engineer and MLOps engineer, while adding ML engineering capabilities to existing experience in software development, DevOps, data engineering or data science. AWS specifically identifies these professional backgrounds for the certification.
Why choose Trainocate?
Trainocate has been recognized as the AWS Training Partner of the Year from 2022-2025, highlighting its excellence in cloud skills development and commitment to digital transformation globally. This award reinforces Trainocate’s reputation as a top-tier AWS training provider, ensuring learners receive industry-recognized education with the latest cloud technologies.
By choosing Trainocate, participants benefit from an award-winning institution dedicated to equipping professionals with in-demand AWS skills to drive career growth and business innovation.
Ready to position yourself for in-demand ML roles?
Complete the form below to register your interest.
Frequently Asked Questions (FAQs)
1. Who should earn AWS Certified Machine Learning Engineer - Associate?
The ideal candidate for this exam has at least 1 year of experience in machine learning engineering or a related field and 1 year of hands-on experience with AWS services. Professionals who do not have prior machine learning experience can take the training available in the Exam Prep Plans and get started building their knowledge and skills.
2. How will the AWS Certified Machine Learning Engineer - Associate help my career?
Per the World Economic Forum Future of Jobs Report 2023, demand for AI and Machine Learning Specialists is expected to grow by 40%. However, 70% of North American IT leaders say they have the greatest difficulty filling AI/ML specialist roles. This certification can position you for in-demand machine learning jobs in AWS Cloud.
3. How is AWS Certified Machine Learning Engineer - Associate different from AWS Certified Machine Learning - Specialty?
AWS Certified Machine Learning Engineer – Associate is a role-based certification designed for ML engineers and MLOps engineers with at least one year of experience in AI/ML.
AWS Machine Learning – Specialty is a specialty certification covering topics across data engineering, data analysis, modeling, and ML implementation and ops. It is more suitable for individuals with 2 or more years of experience developing, architecting, and running ML workloads on AWS.
4. What certification(s) should I earn next after AWS Certified Machine Learning Engineer - Associate?
For professionals looking to dive deeper into machine learning, we recommend AWS Certified Machine Learning – Specialty.
5. How long is this certification valid for?
This certification is valid for 3 years. Before your certification expires, you can recertify by passing the latest version of this exam. Learn more about recertification options for AWS Certifications.
