Why a framework
ML design questions are deliberately vague. A framework keeps you from jumping straight to model architecture and forgetting the parts that actually decide success in production.
The stages
Work top to bottom, narrating trade-offs as you go.
- Clarify the business goal, scale, and constraints before anything else
- Frame the problem as a concrete ML task with inputs and outputs
- Metrics for both the model and the business
- Data sources, labels, and freshness
- Model baseline first, then complexity only if justified
- Serving latency, throughput, batch versus real time
- Evaluation offline and online, including an AB test
- Monitoring drift, failures, and feedback loops
Use a baseline anchor
Always propose a simple baseline first. A heuristic or logistic regression gives you a number to beat and a fallback for launch day.
The order matters: each stage feeds constraints into the next. A latency budget set early eliminates whole classes of models later.
Key idea
Lead with the problem and the metric, not the model. A framework turns a vague prompt into a defensible, ordered argument.