ML in Products
Building ML-powered products.
Feasibility Analysis
Technical feasibility: can we build it? Data feasibility: do we have data? Business value: is it worth it?
Prioritization
Impact vs effort matrix. Technical debt considerations. Quick wins first.
Metrics
Business metrics: revenue, engagement. Model metrics: accuracy, latency. Data metrics: quality, coverage.
Key Takeaways
- Analyze feasibility before building
- Prioritize based on impact
- Align metrics with business goals