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You can share the course access to somebody (e.g. your colleague). An email with direct enrollment link will be
sent in your name. You will not be able to enroll in the course yourself.
Languages Available: Deutsch | Español (Latinoamérica) | Français | ไทย | Italiano | 日本語 | 한국어 | Português (Brasil) | 中文(简体) | 中文(繁體) | Tiếng ViệtThis course introduces requirements to determine if machine learning (ML) is the appropriate solution to a business problem. • Course level: Fundamental • Duration: 30 minutesActivitiesThis course includes presentations, videos, and knowledge assessments.Course objectivesIn this course, you will learn to: • Identify the data, time, and production requirements for a successful ML projectIntended audienceThis course is intended for: • Nontechnical business leaders and other business decision makers who are, or will be, involved in ML projects • Participants of the AWS Machine Learning Embark program, and Machine Learning Solutions Lab (MLSL) discovery workshopsPrerequisitesWe recommend that attendees of this course have: • Introduction to Machine Learning: Art of the PossibleCourse outlineModule 1: Is a machine learning solution appropriate for my problem? • Explain how to determine if ML is the appropriate solution to your business problemModule 2: Is my data ready for machine learning? • Describe the process of ensuring that your data is ML readyModule 3: How will machine learning impact a project timeline? • Explain how ML can impact a project timelineModule 4: What early questions should I ask in deployment? • Identify the questions to ask that affect ML deploymentModule 5: Conclusion