Description
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
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