Building a Machine Learning Ready Organization

Languages Available: Deutsch | Español (Latinoamérica) | Français | ไทย | Italiano | 日本語 | 한국어 | Português (Brasil) | 中文（简体) | 中文（繁體) | Tiếng ViệtCourse descriptionThis course provides components needed for successful organizational adoption of machine learning (ML). • Course level: Fundamental • Duration: 30 minutesActivitiesThis course includes presentations, videos, and knowledge assessments.Course objectivesIn this course, you will learn to: • Describe how to adapt an organization to achieve and sustain success using MLIntended 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 Possible • Planning a Machine Learning ProjectCourse outlineModule 1: How can I prepare my organization for using ML? • How can I prepare my organization for using ML? • How can AWS help me? • What other strategies can I adopt to ensure organizational success? • Which cultural shift-approach works for my organization?Module 2: How do I evaluate my data strategy? • How do I evaluate my data strategy? • How can I improve my data strategy?Module 3: How do I create a culture of learning and collaboration? • How do I create a culture of learning and collaboration? • What is a data scientist? • What skills should a data scientist have? • What does a pilot ML team look like? • What other supporting roles will I need? • What are the key responsibilities?Module 4: How do I start my ML journey? • How do I start my ML journey? • What does an organization’s ML journey look like? • What is an example business case for an organization’s progression?Module 5: Conclusion