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Exam Readiness: AWS Certified Machine Learning - Specialty Training Course

Course Summary

The Exam Readiness: AWS Certified Machine Learning - Specialty training course is designed to demonstrate how to design, implement, deploy, and maintain Machine Learning (ML) solutions for given business problems.

The course begins by examining exam topic areas including data engineering, exploratory data analysis and modeling. Next, it analyzes machine learning implementation and operations. The course concludes with a review of how to interpret exam questions in each topic area and apply the tested concepts.


    • Proficiency expressing the intuition behind basic machine learning algorithms and performing basic hyperparameter optimization

    • Understanding of the machine learning pipeline and its components

    • Experience with machine learning and deep learning frameworks

    • Understanding of and experience in model training, deployment and operational best practices

AWS Authorized Training is only available in Argentina, Brazil, Canada, Chile, Colombia, Costa Rica, Mexico, United States, and Peru.


Help students prepare for the AWS Certified Machine Learning - Specialty exam.
Machine learning practitioners who are preparing to take the AWS Certified Machine Learning - Specialty exam
Software Developer
Skill Level
1 Day
Related Technologies
Machine Learning Training


Productivity Objectives
  • Navigate the logistics of the examination process
  • Examine the exam structure and question types
  • Identify how questions relate to AWS Machine Learning concepts
  • Interpret the concepts being tested by the exam question
  • Allocate your time studying for the AWS Certified Machine Learning -Specialty exam

What You'll Learn:

In the Exam Readiness: AWS Certified Machine Learning - Specialty Training Course training course, you'll learn:
  • Exam oOverview and Test-Taking Strategies
    • Exam overview, logistics, scoring, and user interface
    • Question mechanics and design
    • Test-taking strategies
  • Data Engineering
    • Data repositories for machine learning
    • Identify and implement a data-ingestion solution
    • Identify and implement a data-transformation solution
  • Exploratory Data Analysis
    • Sanitize and prepare data for modeling
    • Perform featuring engineering
    • Analyze and visualize data for machine learning
  • Modeling
    • Frame business problems as machine learning problems
    • Select the appropriate model(s) for a given machine learning problem
    • Train machine learning models
    • Perform hyperparameter optimization
    • Evaluate machine learning models
  • Machine Learning Implementation and Operations
    • Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance
    • Recommend and implement the appropriate machine learning services and features for a given problem
    • Apply basic AWS security practices to machine learning solutions
    • Deploy and operationalize machine learning solutions
  • Additional Study Questions
    • Opportunity to take additional study questions
  • Recommended Study Material
    • Links to AWS blogs
    • Documentation
    • FAQs
    • Other recommended study material for the exam
  • Course Wrap-Up
    • How to sign up for the exam
    • Course summary
    • Course feedback
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”


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