Building & Integrating AI
Building AI Solutions
From a defined problem to a working model and measurable results.
Learning objectives
- Define the problem in measurable terms
- Build a baseline before a sophisticated model
- Iterate on evaluation and improvement
Define the problem
Start with a crisp objective and its success metric. For example, reduce support ticket resolution time by 20% using automated categorization. Vague goals produce unverifiable models.
Start with a baseline
Build the simplest reasonable model first - even a rule-based baseline. A baseline gives you something to beat and calibrates expectations about how much value the data actually contains.
Iterate deliberately
Improve in small, measurable steps: better features, more data, a different algorithm. Keep the evaluation harness fixed so you can trust that each change is real progress.
Key takeaways
- Define measurable success before writing any code.
- Baselines prevent over-engineering.
- Iterate against a fixed evaluation harness.