Planning a Machine Learning Project

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