Planning a Machine Learning Project

This course introduces requirements to determine if machine learning (ML) is the appropriate solution to a business problem.

This course introduces requirements to determine if machine learning (ML) is the appropriate solution to a business problem.

  • Course level: Fundamental
  • Duration: 30 minutes

Activities:
This course includes presentations, videos, and knowledge assessments.

Course objectives:
In this course, you will learn to:

  • Identify the data, time, and production requirements for a successful ML project

Intended audience:
This course is intended for:

  • Nontechnical business leaders and other business decision-makers who are, or will be, involved in ML projects
  • Participants in the AWS Machine Learning Embark program, and Machine Learning Solutions Lab (MLSL) discovery workshops

Prerequisites:
We recommend that attendees of this course have:

  • Introduction to Machine Learning: Art of the Possible

Course outline:
Module 1: Is a machine learning solution appropriate for my problem?

  • Explain how to determine if ML is the appropriate solution to your business problem

Module 2: Is my data ready for machine learning?

  • Describe the process of ensuring that your data is ML-ready

Module 3: How will machine learning impact a project timeline?

  • Explain how ML can impact a project timeline

Module 4: What early questions should I ask in deployment?

  • Identify the questions to ask that affect ML deployment

Module 5: Conclusion

About Instructor

AWS

106 Courses

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FREE

Course Includes

  • 1 Lesson
  • Instructor: AWS

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