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Coursera

AWS Machine Learning Engineer Associate Exam Prep

via Coursera

Overview

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This course prepares learners for the AWS Certified Machine Learning Engineer – Associate certification while building practical skills in implementing and operationalizing machine learning workloads on AWS. Through a combination of clear theory, architectural diagrams, and hands-on demonstrations, the course explains how machine learning solutions are designed, deployed, and managed in real-world cloud environments. Learners will explore the key concepts required for building production-ready ML systems, including data preparation, model training, deployment strategies, and monitoring ML workloads in AWS. The course also introduces the AWS services and tools commonly used to develop scalable and reliable machine learning solutions. By the end of the course, learners will understand how to implement and manage ML workflows on AWS and will be well prepared to take the AWS Certified Machine Learning Engineer – Associate exam.

Syllabus

  • Let's Get Started
  • Data Storage/Ingestion
  • Exploratory Data Analysis
  • Machine Learning
  • Managed AI Services
  • Modelling (SageMaker Built-In Algorithms)
  • Amazon SageMaker Services
  • Model Deployment
  • AWS Infrastructure, MLOps, and Orchestration
  • Foundation Models and Applications
  • AWS GenAI Services and Infrastructure
  • Monitoring and Optimization

Taught by

Neal Davis and Karim ElKobrossy

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