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How do I safely roll out a new model version?
Gradual rollout is the safest approach. Deploy the new model alongside the old one and route a small percentage of traffic to it. Monitor key metrics before increasing exposure. Fallback mechanisms are essential—rollback should be instant and automated. Common mistakes: Full replacement deploymentsRead more
Gradual rollout is the safest approach. Deploy the new model alongside the old one and route a small percentage of traffic to it. Monitor key metrics before increasing exposure.
Fallback mechanisms are essential—rollback should be instant and automated.
Common mistakes:
Full replacement deployments
Missing rollback plans
Monitoring only aggregate metrics
Production models should evolve cautiously
See lessHow can batch size changes affect model convergence?
Batch size directly influences gradient noise and optimization dynamics. Smaller batches introduce stochasticity that can help generalization, while larger batches provide stable but potentially brittle updates. Changing batch size without adjusting learning rate often breaks convergence. If you incRead more
Batch size directly influences gradient noise and optimization dynamics.
Smaller batches introduce stochasticity that can help generalization, while larger batches provide stable but potentially brittle updates.
Changing batch size without adjusting learning rate often breaks convergence. If you increase batch size, scale the learning rate proportionally or use adaptive optimizers.
Common mistakes:
Changing batch size mid-training
Comparing results across different batch regimes
Assuming larger batches are always better
Batch size is a training hyperparameter, not just a performance knob.
See lessWhat causes “CUDA out of memory” errors even with a small batch size?
This usually happens because memory is being accumulated across iterations rather than freed correctly. The most common cause is storing computation graphs unintentionally, often by appending loss tensors or model outputs to a list without detaching them. Over time, GPU memory fills up regardless ofRead more
This usually happens because memory is being accumulated across iterations rather than freed correctly.
The most common cause is storing computation graphs unintentionally, often by appending loss tensors or model outputs to a list without detaching them. Over time, GPU memory fills up regardless of batch size.
Make sure you call
optimizer.zero_grad()every iteration and avoid saving tensors that require gradients. If you need to log values, convert them to scalars using.item().In transformer workloads, sequence length matters more than batch size. A batch of 2 with long sequences can exceed memory limits faster than a batch of 16 with shorter inputs.
Common mistakes:
Forgetting
torch.no_grad()during evaluationLogging full tensors instead of scalars
Increasing max token length without adjusting batch size
Monitoring GPU memory with a profiler will usually reveal the leak within a few iterations.
See lessWhy does my model fail only on edge cases?
Edge cases are often underrepresented during training. The model optimizes for majority patterns and lacks exposure to rare scenarios. This is common in NLP, fraud detection, and vision tasks. Augment training data with targeted edge examples and weight them appropriately. Common mistakes: AssumingRead more
Edge cases are often underrepresented during training. The model optimizes for majority patterns and lacks exposure to rare scenarios. This is common in NLP, fraud detection, and vision tasks. Augment training data with targeted edge examples and weight them appropriately.
Common mistakes:
Assuming edge cases don’t matter
Treating all samples equally
Not logging failure cases
Production failures usually live at the edges.
See lessWhy does my model’s confidence increase while accuracy decreases?
The model is becoming more certain about wrong predictions, often due to overfitting or distribution shift. This is especially common after retraining or fine-tuning on narrow datasets. Measure calibration metrics like expected calibration error (ECE) and inspect confidence histograms. Techniques suRead more
The model is becoming more certain about wrong predictions, often due to overfitting or distribution shift. This is especially common after retraining or fine-tuning on narrow datasets. Measure calibration metrics like expected calibration error (ECE) and inspect confidence histograms. Techniques such as temperature scaling or label smoothing can restore better alignment between confidence and correctness.
Common mistakes:
Equating confidence with correctness
Monitoring accuracy without calibration
Deploying fine-tuned models without recalibration
A trustworthy model knows when it might be wrong.
See less