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Machine learning on AWS | ML Specialty, ML-minded, for technical people..
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The AWS Certified Machine Learning Engineer - Associate (MLA-C01) is AWS's certification for people who build, deploy and operate machine- learning systems on AWS. It proves you can prepare data for ML, develop and train models, deploy and orchestrate them, and monitor, maintain and secure them in production - the practical MLOps work. The updated content also covers generative AI, using services like Amazon SageMaker and Amazon Bedrock. Please note two things. First, AWS has announced an updated exam, MLA-C02, with a changeover in late 2026; as of this guide, MLA-C01 is the current exam, so confirm on aws.amazon.com which exam is live before booking. Second, this certification is the successor to the older AWS Certified Machine Learning - Specialty (MLS-C01), which AWS is retiring; and it is more hands-on and technical than our AWS Certified AI Practitioner (which is a foundational, conceptual certification). You will cover four domains: Data Preparation for ML; ML Model Development; Deployment and Orchestration of ML Workflows; and ML Solution Monitoring, Maintenance and Security. Note that deployment, monitoring and security together make up nearly half the exam - this is about production ML, not just training models.
Machine learning and generative AI are among the most in-demand skills in tech, and shipping them into production is where the value is. The ML Engineer - Associate proves you can build, deploy and operate ML systems on AWS - the skill employers hire ML and MLOps engineers for. It completes the path above our AI Practitioner course, pairs with Data Engineer, and is the current AWS certification for machine-learning engineering.
Source: Glassdoor
Source: Indeed
Annual Salary
Source: Glassdoor
Hiring Companies
Source: Indeed
Batch starting from:
AWS suggests around a year in a related role (developer, DevOps, data engineer or data scientist) plus AWS ML experience. There is no hard prerequisite - anyone can register - but this is not a beginner or non-technical course. If you want AI literacy first, take our AWS Certified AI Practitioner; if you are new to AWS entirely, start with Cloud Practitioner. Two notes: (1) AWS has announced an updated exam, MLA-C02, for late 2026; as of now, MLA-C01 is the current exam - confirm on aws.amazon.com which is live. (2) This certification is the successor to the retiring AWS Certified Machine Learning - Specialty (MLS-C01). If you already hold the ML - Specialty, it remains valid until its expiry date.
How to earn it:
1. Prepare with an ML Engineer - Associate exam-prep course (self-paced, online) - that is what we provide.
2. Create an AWS Certification account and register for the current exam (MLA-C01, or MLA-C02 if it has gone live). Schedule it at a Pearson VUE test cent...
The AWS Certified Machine Learning Engineer certification validates your ability to build, deploy, and maintain machine learning solutions on AWS. This credential demonstrates your expertise in data preparation, model development, deployment, monitoring, security, and machine learning operations using AWS services. This section covers everything you need to know about the AWS Certified Machine Learning Engineer exam, including the exam format, eligibility requirements, certification process, passing criteria, certification validity, and frequently asked questions, helping you prepare with confidence.
Exam details verified on 6 August 2026. Of the 65 questions, 15 are unscored trial questions that do not affect your result. This exam uses some newer question types (ordering, matching and case study) as well as multiple-choice and multiple-response. AWS has announced an updated exam (MLA-C02) with a changeover in late 2026 - as of this guide, MLA-C01 is the current exam. Confirm which exam is live at aws.amazon.com/certification before booking.
This is a mid-level, technical certification for people who build and ship ML on AWS: - Machine learning and MLOps engineers - Data scientists moving into production ML - Developers and data engineers moving into ML - AI Practitioner holders ready for the hands-on step up
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