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What Is Audience for AWS Machine Learning Specialty Certification?

The AWS Certified Machine Learning Specialty certificate is intended for programmers, data scientists, and other candidates passionate about machine learning who want to learn how to use the benefits of artificial intelligence capabilities on the AWS platform. The training process necessary for obtaining this certification helps examinees develop the right skills to build, train, and deploy machine learning models using advanced AWS Cloud services. Candidates can achieve this certificate by obtaining the passing score in MLS-C01 exam. Even though this test doesn't have any mandatory requirements, the vendor recommends that candidates should have previous knowledge of certain topics. A successful applicant is one who has between 1 to 2 years of practical experience in developing, running and architecting ML and deep learning workloads on the AWS Cloud. Also, it would be helpful if the candidate would have prior experience performing basic hyperparameter optimization and know how to follow model-training and operational best practices.

Reference: https://d1.awsstatic.com/training-and-certification/docs-ml/AWS%20Certified%20Machine%20Learning%20-%20Specialty_Exam%20Guide%20(1).pdf

What Topics Are Covered in AWS Machine Learning - Specialty Certification Exam?

The certification exam for the AWS Machine Learning – Specialty certification tests the candidates' ability to select the best machine learning strategy to improve the business processes. Also, they will be able to identify the best AWS service needed to implement different machine learning solutions. Besides, candidates will be able to design and put into practice reliable, scalable, and cost-optimized machine learning solutions. The AWS MLS-C01 exam, in particular, focuses on four domains. They are the following:

• Data engineering;
• Exploratory Data Analysis;
• Modeling;
• Machine Learning Implementation and Operations.

The first topic handles data engineering and has 3 sections. The first one handles the creation of data repositories for efficient machine learning strategies. Also, candidates will learn how to effectively identify and implement solutions related to data-ingestion. The third sub-domain included in this section focuses on the implementation and identification of different data-transformation solutions.

The second tested area concentrates on exploratory data analysis. When they prepare for this topic, candidates will learn how to sanitize and prepare data for modeling. Also, they will learn how to make a performance when it comes to feature engineering. Finally, candidates will learn how to visualize data and analyze different parameters for machine learning.

Within the modeling section, candidates will learn how to frame different business problems related to machine learning issues. Besides, they will find how to select the right models for different machine learning problems. In this section, candidates will also learn how to train effectively for machine learning models. Another essential concept that candidates will discover in this part is related to hyperparameter optimization performance. Last but not least, applicants will understand how to correctly evaluate machine learning models.

The final domain concentrates on machine learning operations and implementation. This segment focuses on helping candidates develop advanced abilities in building machine learning solutions to achieve the highest performance and fault tolerance. These solutions will help them ensure availability, resilience, and scalability. Another subtopic included in the last objective deals with the recommendations and implementation of the right machine learning services adapted to the business context. Candidates will also learn how to apply fundamental AWS security practices to solve different machine learning issues. Finally, they will become ready to deploy and operationalize various machine learning solutions.