Start by outlining the data preprocessing steps to clean and segment the telemetry, then describe how to build a piecewise linear discharge curve from historical data. Explain how to use interpolation to estimate time-to-empty from the current 37% charge, and discuss handling of irregular timestamps, charging periods, and screen-off segments.
Pro tip: Emphasize that the baseline should be simple and interpretable, and that handling edge cases like charging and screen-off is crucial for robustness. Mention that you would validate the estimator against held-out data and consider confidence intervals.
Filter out charging periods and screen-off segments, and resample irregular timestamps to a regular grid (e.g., 1-minute intervals) using interpolation.
From historical discharge data, compute the average discharge rate or battery percentage over time, and construct a piecewise linear function mapping battery percentage to time-to-empty.
Given current charge (37%), interpolate on the piecewise linear curve to estimate remaining time, and optionally adjust for recent usage patterns.
For irregular timestamps, use interpolation or binning; for short charging periods, exclude them from discharge curve; for screen-off, either exclude or model separately as they may have different discharge rates.
Validate the estimator using hold-out data, compute error metrics (e.g., MAE), and consider improvements like separate curves for screen-on/off or time-of-day effects.
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Rattled off a bunch: rolling discharge slope over the last N minutes (steeper slope means less time remaining), screen-on duty cycle, brightness-weighted screen time, CPU utilization moving average, thermal state (higher temp accelerates discharge), foreground app category encoded as activity intensity, network type as a proxy for radio power draw, and battery health degradation as a scalar penalty.
Start by clarifying the problem context: time-to-empty prediction for a battery or similar system, where the raw discharge curve alone is insufficient. Then, systematically list engineered features from different categories (e.g., temporal, statistical, contextual, usage patterns) and for each, explain how it would shift the estimate (e.g., higher temperature accelerates discharge, so it decreases time-to-empty). Finally, emphasize that the direction of effect should be grounded in domain knowledge and validated empirically.
Pro tip: Tie each feature to a real-world mechanism and mention that you would validate the expected direction with feature importance or partial dependence plots to ensure the model learns sensible relationships.
Confirm that the goal is to predict remaining time until empty (e.g., battery) and that the raw discharge curve is the only current input. Acknowledge that additional features can capture external and internal factors affecting discharge rate.
Group features into categories: temporal (e.g., time of day), environmental (e.g., temperature), usage (e.g., load), and derived statistical (e.g., variance of recent voltage). This ensures comprehensive coverage.
For each feature, state its name, how it is computed, and the expected direction of its effect on time-to-empty (e.g., higher temperature → shorter time-to-empty). Aim for at least 8 features.
Mention that you would validate the direction and importance of features using model interpretability tools (e.g., SHAP, permutation importance) and iterate on feature engineering.
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I went with a kNN-style approach using model family, battery health age, and a usage mix vector as the similarity space, then weighted-average the neighbor curves by inverse distance.
Frame the problem as a transfer learning task where you leverage discharge curves from similar devices to build a prior for the new device. Define a similarity metric based on device features and curve shape, combine neighbor curves via weighted averaging or Bayesian hierarchical modeling, and quantify uncertainty using posterior variance or conformal prediction intervals.
Pro tip: Emphasize that uncertainty quantification is critical for cold-start predictions—use it to decide when to fall back to a conservative prior or request more data. Also, mention that similarity should be validated on held-out devices to avoid overfitting to irrelevant features.
Identify relevant device features (e.g., battery capacity, chemistry, usage patterns) and curve characteristics (e.g., shape, inflection points). Use a combination of domain knowledge and data-driven methods (e.g., dynamic time warping, feature embeddings) to compute similarity scores.
Choose top-k similar devices based on the similarity metric. Assign weights proportional to similarity (e.g., softmax over negative distances) to emphasize more similar devices.
Aggregate the discharge curves of selected neighbors using weighted averaging, or fit a hierarchical Bayesian model where device-specific curves are drawn from a common prior. This yields a prior predictive distribution for the new device.
Estimate uncertainty via the variance of the weighted neighbor curves, posterior predictive variance from the Bayesian model, or conformal prediction intervals. Validate calibration on held-out devices.
As new data arrives, update the prior using online learning or Bayesian updating to refine predictions and reduce uncertainty.
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Session-level folds felt obvious to me so I led with that.
Start by defining the offline evaluation protocol: data splitting, metrics, and validation. Then discuss primary and secondary metrics, and finally outline strategies to detect and mitigate systematic underestimation of time-to-empty, such as bias analysis and calibration.
Pro tip: Emphasize the importance of aligning offline metrics with online business objectives and using techniques like quantile loss or asymmetric loss to directly address underestimation bias.
Specify the data splitting strategy (e.g., time-based split to mimic deployment) and ensure no leakage. Consider using a holdout set or cross-validation with time series awareness.
Choose primary metrics that directly reflect the prediction goal (e.g., MAE, RMSE for time-to-empty) and secondary metrics for robustness (e.g., bias, quantile loss, calibration).
Use asymmetric loss functions (e.g., quantile loss at high quantile) or post-hoc calibration. Analyze residuals to detect systematic bias and consider stratification by relevant segments.
Perform error analysis, compare against baselines, and simulate online impact. Use techniques like bootstrapping to estimate confidence intervals for metrics.
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Talked about tracking the distribution of discharge slopes over rolling windows and flagging when it drifts outside a historical baseline, and also monitoring ambient temperature distributions since seasonal shifts affect battery behavior.
Start by defining the monitoring strategy: track input feature distributions and output predictions using statistical tests and drift metrics, with alerts for significant deviations. Then describe an automatic recalibration procedure that triggers retraining or model updates when drift is detected, ensuring minimal manual intervention and maintaining performance.
Pro tip: Emphasize the importance of setting up a feedback loop with ground truth labels (e.g., from delayed discharge outcomes) to validate drift and recalibration effectiveness, and discuss trade-offs between frequent recalibration and operational costs.
Identify key input features (e.g., patient demographics, clinical indicators) and output distributions (e.g., discharge disposition) to monitor. Choose appropriate statistical tests (e.g., KS test, PSI) and set thresholds for alerts.
Set up pipelines to compute drift metrics on a schedule (e.g., daily/weekly) and visualize trends. Use tools like TensorFlow Data Validation or custom dashboards to track changes over time.
When drift is detected, analyze which features or predictions are affected and root causes (e.g., changes in hospital policies, patient mix). Correlate with external events to confirm.
Define triggers for recalibration (e.g., drift magnitude, performance degradation). Implement a pipeline that automatically retrains the model on recent data, validates it, and deploys if performance improves.
After recalibration, monitor model performance and drift to ensure effectiveness. Use A/B testing or shadow deployment to compare new vs. old model. Continuously refine thresholds and procedures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.