5/15/2025·1 min read
Choosing the Right Machine Learning Model for Your Startup
MLproductmodels
A constraint‑driven playbook for model selection that stays useful as your product evolves.
- Start simple; complexity is a cost
- Choose metrics that reflect user value
- Design for monitoring from day one
Start with constraints: latency, cost, accuracy, data size, privacy. Then pick the simplest model that can work.
- Tabular data: gradient boosting (XGBoost, LightGBM) is a strong baseline.
- Language: retrieval + LLM (few-shot or function calling) before fine-tuning.
- Vision: pre-trained backbones + light adapters.
Maintain evals that reflect user value, not just benchmarks.
Frame the decision with constraints
Latency, cost per request, privacy, and expected accuracy define your feasible set. Write them down and design experiments to test trade‑offs.
- Tabular → gradient boosting
- Language → retrieval + LLM before fine‑tune
- Vision → pre‑trained backbones + adapters
Operational excellence matters
Create small, realistic eval sets. Automate runs in CI so regressions surface fast. Track drift and build a rollback path.
Key takeaways
- Pick the simplest model that meets constraints
- Automate evals and monitoring
- Focus on product impact, not leaderboard scores
