I went straight to accuracy and then kind of rambled into precision and recall without really anchoring it to a use case first.
Start by clarifying the model's objective and the business context, then propose a layered evaluation framework that includes offline metrics, online metrics, and business KPIs. Emphasize the importance of aligning metrics with Apple's product goals, such as user privacy, on-device performance, and seamless user experience.
Pro tip: Demonstrate awareness of Apple's privacy constraints by mentioning on-device evaluation and federated learning metrics, and highlight the trade-off between model complexity and latency on edge devices.
Ask about the model's purpose, target users, and what business or user problem it solves. Define what success looks like in terms of user impact and technical constraints.
Choose metrics that reflect the model's predictive quality on held-out data, such as accuracy, precision/recall, F1, AUC-ROC, or task-specific metrics like BLEU for NLP. Consider calibration and robustness.
Propose A/B testing metrics like click-through rate, conversion rate, user engagement, and retention. Tie these to business KPIs such as revenue, user satisfaction, or reduced churn.
Include latency, memory footprint, battery consumption, and model size, especially for on-device deployment. Discuss trade-offs between accuracy and efficiency.
Describe how you would monitor metrics post-deployment, detect drift, and set up feedback loops for continuous improvement, ensuring alignment with privacy-preserving techniques.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.