Übersicht
This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
Lernziele
In this course, you will learn to:
Hinweis
Jetzt bequem von zu Hause aus, im Büro oder in jedem AddOn Training Center an Virtual Classroom Trainings teilnehmen. Hier erfahren Sie mehr zum Virtual Classroom.
Zielgruppe
Developers
Voraussetzungen
We recommend that attendees of this course have:
Inhalte
- In this course, you will learn to:
- 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 using 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
Preis: 0,00 CHF
Termine
