AWS Certified Machine Learning - Specialty

Introduction

The AWS Certified Machine Learning – Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence/machine learning (AI/ML) development or data science role. The exam validates a candidate’s ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud. The exam also validates a candidate’s ability to complete the following tasks:

  • Select and justify the appropriate ML approach for a given business problem
  • Identify appropriate AWS services to implement ML solutions
  • Design and implement scalable, cost-optimized, reliable, and secure ML solutions

Exam Format

65 questions; either multiple choice or multiple response

180 mins to complete the exam

Pre-requisites

The target candidate is expected to have 2 or more years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud.

Recommended AWS knowledge

The target candidate should have the following knowledge:

  • The ability to express the intuition behind basic ML algorithms
  • Experience performing basic hyperparameter optimization
  • Experience with ML and deep learning frameworks
  • The ability to follow model-training best practices
  • The ability to follow deployment best practices
  • The ability to follow operational best practices

Target Audience

AWS Certified Machine Learning Specialty exam is for:

  • Candidates who are interested in Data Science.
  • Business Decision Makers
  • Developers
  • Data Platform Engineers
  • One who is pursuing to build a career in ML

Course Syllabus

Domain 1: Data Engineering

  • 1.1 Create data repositories for machine learning
  • 1.2 Identify and implement a data ingestion solution
  • 1.3 Identify and implement a data transformation solution

Domain 2: Exploratory Data Analysis

  • 2.1 Sanitize and prepare data for modeling.
  • 2.2 Perform feature engineering.
  • 2.3 Analyze and visualize data for machine learning.

Domain 3: Modeling

  • 3.1 Frame business problems as machine learning problems
  • 3.2 Select the appropriate model(s) for a given machine learning problem.
  • 3.3 Train machine learning models
  • 3.4 Perform hyperparameter optimization.
  • 3.5 Evaluate machine learning models.

Domain 4: Machine Learning Implementation and Operations

  • 4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
  • 4.2 Recommend and implement the appropriate machine learning services and features for a given problem
  • 4.3 Apply basic AWS security practices to machine learning solutions.
  • 4.4 Deploy and operationalize machine learning solutions.