Edchart Certified AWS Machine Learning Subject Matter Expert

Issued by EDCHART INC

The AWS Machine Learning Certification by EdChart validates expertise in building, deploying, and managing machine learning models on AWS. This certification covers key areas such as data engineering, model training, tuning, and deployment using AWS services like SageMaker, Lambda, and AI/ML frameworks. It is ideal for professionals seeking to enhance their skills in AI-driven solutions and leverage AWS cloud infrastructure for scalable machine learning applications.

Type Certification
Level Advanced
Time Hours
Cost Paid

Additional Details

Skills

AWS Certified Machine Learning – Specialty
AWS Machine Learning
AWS Machine Learning Certification
Enterprise Machine Learning with AWS
Machine Learning (ML)

Earning Criteria

To earn the AWS Machine Learning Certification, candidates must pass an online exam assessing their ability to implement ML solutions on AWS. The exam tests skills in problem framing, data preprocessing, model selection, hyperparameter tuning, and deploying ML models. A strong understanding of AWS services, security considerations, and best practices in AI/ML is necessary to meet the certification requirements.
This certification is designed for data scientists, machine learning engineers, AI developers, and cloud professionals looking to specialize in AWS-powered ML solutions. It is also suitable for IT professionals transitioning into AI/ML roles. Candidates with a background in Python, statistics, and cloud computing will benefit the most from this certification.
While there are no strict educational prerequisites, candidates should have foundational knowledge of machine learning concepts, cloud services, and Python programming. Experience with AWS tools like SageMaker, EC2, S3, and Lambda is recommended. A background in mathematics, data science, or computer science will be helpful in understanding key ML concepts.

Standards

AWS Machine Learning Certification

To meet certification standards, candidates must demonstrate proficiency in designing and deploying ML models on AWS, optimizing model performance, and ensuring security in ML workflows. Strong analytical skills, cloud infrastructure knowledge, and the ability to automate ML pipelines are essential.