Data scientists and ML engineers developing and deploying ML solutions on AWS. Developers and architects integrating ML models into cloud-based applications. AI researchers and IT professionals interested in advanced ML techniques.
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.
The exam also validates a candidate's ability to complete the following tasks:
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This comprehensive course is designed to equip participants with the knowledge and skills necessary to excel in the AWS Certified Machine Learning Specialty exam. Through a combination of theoretical lectures, hands-on labs, and practical exercises, participants will delve into key machine learning concepts, AWS services, and best practices required to tackle real-world machine learning challenges on the AWS platform.
The learning objectives for the AWS Certified Machine Learning Engineer Associate and AWS Certified Machine Learning Specialty certifications include:
Benefits of the AWS Certified Machine Learning Engineer Associate certification include:
You'll receive a digital badge to showcase your achievement online
If you earn the certification by February 15, 2025, you'll also receive a special Early Adopter badge.
If you fail your first attempt at the exam before Feb 15, 2025, you can retake it for free.
You can get a 50% discount voucher for recertification or other exams.
You can receive recognition at events.
You'll be prepared for roles like ML engineer and ML Ops engineer.
You'll have skills that can help you serve more users, reduce costs, and get faster results.
As of 2024, AWS does not currently offer a certification explicitly titled "Associate Machine Learning Engineer." However, AWS provides the AWS Certified Machine Learning – Specialty certification, which is designed for professionals aiming to demonstrate expertise in building, training, tuning, and deploying machine learning (ML) models on AWS.
AWS-related job postings globally
1.2M+Annual AWS usage growth rate
37%Top AWS clients in North America
53%
AWS certifications, including the Machine Learning Engineer - Associate (MLA-C01), are highly sought-after in today's tech landscape. With consistent growth in the cloud industry, AWS skills are essential for individuals looking to advance in cloud and IT careers. The demand for AWS-certified professionals has seen a significant year-over-year increase, reflecting the industry's reliance on AWS infrastructure.
of Fortune 500 companies utilize AWS services, creating a vast array of career opportunities for AWS-certified professionals.
The average annual salary for an AWS Certified Cloud Practitioner in the U.S., reflecting strong demand and value for foundational cloud skills.
Expected annual growth in cloud computing jobs globally over the next 5years, fueled by AWS's market leadership.
The target candidate should have at least 1 year of experience using Amazon SageMaker and other AWS services for ML engineering. The target candidate also should have at least 1 year of experience in a related role such as a backend software developer, DevOps developer, data engineer, or data scientist.
The target candidate should have the following general IT knowledge:
The target candidate should have the following AWS knowledge
The following list contains job tasks that the target candidate is not expected to be able to perform. This list is non-exhaustive. These tasks are out of scope for the exam: Designing and architecting full end-to-end ML solutions
AWS Machine Learning Engineer Course prepares you for the AWS Certified Machine Learning Certification. Gain expertise in designing, training, and deploying machine learning models on AWS. Leverage Simplilearn's Job Assistance Services to enhance your career prospects, ensuring readiness for advanced roles such as Machine Learning Engineer, AI Specialist, or Data Scientist.
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Here you'll find answers to the most commonly asked questions about our services, products, and expertise.
Data scientists and ML engineers developing and deploying ML solutions on AWS. Developers and architects integrating ML models into cloud-based applications. AI researchers and IT professionals interested in advanced ML techniques.
Deep learning uses neural networks to process complex datasets, while traditional ML focuses on simpler algorithms for smaller datasets.
Python, R, Java, and C++ are popular languages. Python is the most commonly used due to its rich ecosystem of ML libraries.
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