Exam Readiness: AWS Certified Machine Learning - Specialty (ERMLS)
Who should attend
This course is intended for:
- Machine learning practitioners preparing to take the AWS Certified Machine Learning – Specialty exam
This course is part of the following Certifications:
We recommend that attendees of this course to have:
- One or two years of hands-on experience developing, architecting, or running ML/deep learning workloads on the AWS cloud.
- Proficiency at expressing the intuition behind basic ML algorithms and performing basic hyperparameter optimization
- Understanding of complete ML pipeline and its components
- Experience with ML and deep learning frameworks
- Understanding and applying model training, deployment and operational best practices
This course is designed to teach you how to:
- Identify their strengths and weaknesses in each of the exam domains.
- Create a subsequent study plan to prepare for the exam.
- Describe the technical topics and concepts making up each of the exam domains.
- Summarize the logistics and mechanics of the certification exam and its questions.
- Identify effective test taking strategies that can be used to answer exam questions.
The AWS Certified Machine Learning – Specialty exam validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) or deep learning (DL) solutions for given business problems.
People with one to two years of experience developing, architecting, or running ML/DL workloads on the AWS cloud should join this workshop to learn how to prepare to successfully pass the exam.
The workshop explores the exam’s topic areas, shows how they relate to machine learning on AWS, and also maps them to machine learning and deep learning foundational areas for future self-study. It includes sample exam questions from each domain and discussions of concepts being tested to help test-takers better eliminate incorrect responses.
Topics in the course will address each of the exam’s four subject domains. 1. Data Engineering 2. Exploratory Data Analysis 3. Modeling 4. Machine Learning Implementation and Operations