
Srinivasan Lingaraj
Solutions Architect
Course Overview:
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 of 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 deploymentModule 5: Conclusion
Basic Knowledge of AWS Services and SQL Solutions Architect AWS Technical Trainer The Upskilled platform works best with current versions of Chrome, Edge, Firefox, or Safari. See our list of supported browsers for the most up-to-date information.About This Course
Requirements
Course Staff
Srinivasan Lingaraj
Mohammed Arief M
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