Planning a Machine Learning Project

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