Frame AI Problems: Objectives to Metrics

Successful AI projects start with clarity, not code. This short, hands-on course helps you turn vague business goals into structured, measurable, and feasible AI problem statements. You’ll learn to evaluate whether your data is ready for modeling, estimate labeling requirements, and identify early risks such as imbalance, poor quality, or limited resources. Using real-world scenarios, you’ll apply the SMART framework to define objectives that are specific, measurable, achievable, relevant, and time-bound. By connecting business outcomes with technical success metrics like precision and recall, you’ll gain the confidence to frame AI projects that deliver measurable impact and align teams from idea to implementation

• Evaluate data readiness for modeling and estimate labeling needs
• Identify early risks including imbalance and poor data quality
• Apply SMART framework to define clear and achievable objectives
• Connect business outcomes with technical success metrics for project alignment

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work