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Why do online and batch predictions disagree?
Differences usually stem from data freshness or preprocessing timing. Batch jobs often use historical snapshots, while online systems use near-real-time data. Feature values may differ subtly but significantly. Ensure both paths use the same feature definitions and time alignment rules. The takeawayRead more
Differences usually stem from data freshness or preprocessing timing.
Batch jobs often use historical snapshots, while online systems use near-real-time data. Feature values may differ subtly but significantly.
Ensure both paths use the same feature definitions and time alignment rules.
The takeaway is that consistency requires shared assumptions across modes.
See lessWhy does autoscaling my inference service increase latency?
Autoscaling can introduce cold start penalties if not tuned correctly. Model loading and initialization are often expensive. When new instances spin up under load, they may take seconds to become ready, increasing tail latency. Pre-warm instances or use minimum replica counts to avoid frequent coldRead more
Autoscaling can introduce cold start penalties if not tuned correctly.
Model loading and initialization are often expensive. When new instances spin up under load, they may take seconds to become ready, increasing tail latency.
Pre-warm instances or use minimum replica counts to avoid frequent cold starts. Also measure model load time separately from inference time.
For large models, consider keeping them resident in memory or using dedicated inference services.
See lessWhy does my model accuracy degrade only for specific user segments?
Segment-specific degradation often indicates biased or underrepresented training data. Certain user groups may appear rarely in training but frequently in production. As a result, the model generalizes poorly for them. Break down metrics by meaningful segments such as geography, device type, or behaRead more
Segment-specific degradation often indicates biased or underrepresented training data.
Certain user groups may appear rarely in training but frequently in production. As a result, the model generalizes poorly for them.
Break down metrics by meaningful segments such as geography, device type, or behavior patterns. This often reveals hidden weaknesses.
Consider targeted data collection or separate models for high-impact segments.The takeaway is that averages hide important failures
See lessHow should I version models when code, data, and parameters all change?
Model versioning must include more than just the model file. A reliable version should uniquely identify the training code, dataset snapshot, feature logic, and configuration. Hashes or version IDs tied to these components help ensure traceability. Store model metadata alongside artifacts, includingRead more
Model versioning must include more than just the model file.
A reliable version should uniquely identify the training code, dataset snapshot, feature logic, and configuration. Hashes or version IDs tied to these components help ensure traceability.
Store model metadata alongside artifacts, including training time, data ranges, and metrics. This makes comparisons and rollbacks predictable.
Avoid versioning models based only on timestamps or manual naming conventions.
Common mistakes include:
Versioning only the
.pklor.ptfileLosing track of training data versions. Overwriting artifacts in shared storage
The practical takeaway is that a model version is a system snapshot, not just weights.
See lessHow can I detect data drift without labeling production data?
You can detect data drift without labels by monitoring input distributions. Track statistical properties of each feature and compare them to training baselines. Significant changes in distributions, category frequencies, or missing rates are often early indicators of performance degradation. Use metRead more
You can detect data drift without labels by monitoring input distributions.
Track statistical properties of each feature and compare them to training baselines. Significant changes in distributions, category frequencies, or missing rates are often early indicators of performance degradation.
Use metrics like population stability index (PSI), KL divergence, or simple threshold-based alerts for numerical features. For categorical features, monitor new or disappearing categories.
This won’t tell you exact accuracy, but it provides a strong signal that retraining or investigation is needed.The key takeaway is that unlabeled drift detection is still actionable and essential in production ML
See lessWhy does my model overfit even with regularization?
Overfitting can persist if data leakage or feature shortcuts exist. Check whether features unintentionally encode target information or future data. Regularization can’t fix fundamentally flawed signals. Also examine whether validation data truly represents unseen scenarios. Common mistakes include:Read more
Overfitting can persist if data leakage or feature shortcuts exist. Check whether features unintentionally encode target information or future data. Regularization can’t fix fundamentally flawed signals.
Also examine whether validation data truly represents unseen scenarios. Common mistakes include: Trusting regularization blindly, Ignoring feature leakage, Using weak validation splits
The takeaway is that overfitting is often a data problem, not a model one.
See less