• Classroom
  • Online, Instructor-Led
Course Description

Learn how to use the machine learning (ML) pipeline with Amazon SageMaker with hands-on exercises and four days of instruction. You will learn how to frame your business problems as ML problems and use Amazon SageMaker to train, evaluate, tune, and deploy ML models. Hands-on learning is a key component of this course, so you’ll choose a project to work on, and then apply the knowledge and skills you learn to your chosen project in each phase of the pipeline. You’ll have a choice of projects: fraud detection, recommendation engines, or flight delays.

Learning Objectives

Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model in Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Cyber Defense Analysis
  • Cyber Defense Infrastructure Support
  • Cyber Investigation
  • Digital Forensics
  • Exploitation Analysis
  • Network Services
  • Risk Management
  • Software Development
  • Test and Evaluation
  • Threat Analysis
  • Training, Education, and Awareness
  • Vulnerability Assessment and Management