
AWS Certs: ML Engineer Associate vs ML Speciality. What's the difference?
This post compares the AWS ML Engineer Associate and ML Specialty certifications, highlighting their target audiences, skill levels, and focus areas to help you choose.
- ML Engineer Associate: The MLE Associate certification is ideal for candidates with at least one year of experience in Machine Learning or a related field. This certification is more accessible to those who are earlier in their careers or transitioning into the ML space, as it allows professionals without prior ML experience to gain knowledge through available training and exam prep resources. The focus here is on building and operating ML applications on AWS.
- ML Specialty: This certification is designed for individuals with at least two years of hands-on experience developing, architecting, and running machine learning (ML) or deep learning workloads in the AWS Cloud. It’s aimed at those who can express the intuition behind basic ML algorithms, perform hyperparameter optimization, work with ML and deep learning frameworks, and follow best practices for model training, deployment, and operations. The MLS certification is perfect for professionals already well-versed in ML on AWS and looking to validate their deep technical expertise.
- ML Engineer Associate: This certification focuses on understanding, determining, using, and classifying knowledge related to machine learning on AWS. The questions will test candidates’ ability to comprehend core concepts, implement solutions effectively, and solve practical problems in ML engineering. Expect varied complexity like in any Associate exam (Intermediate level)
- ML Specialty: The exam requires candidates to analyze, evaluate, and assess complex ML scenarios. This involves a deep understanding of ML algorithms, the ability to evaluate different approaches critically, and making informed decisions in specialized domains; questions are generally longer and require deep knowledge (Like in most Professional/Speciality exams).