The Controller layer focuses only on exposing data to LWC or Aura components.It delegates all business decisions to the Service layer.It also ensures users receive consistent, friendly error messages.This responsibility split is often highlighted when learning UI-to-service separation through SalesfRead more
The Controller layer focuses only on exposing data to LWC or Aura components.
It delegates all business decisions to the Service layer.
It also ensures users receive consistent, friendly error messages.
This responsibility split is often highlighted when learning UI-to-service separation through SalesforceTrail scenarios.
Why does my inference latency increase after model optimization?
Some optimizations improve throughput but hurt single-request latency. Batching, quantization, or graph compilation can introduce overhead that only pays off at scale. In low-traffic scenarios, this overhead dominates. Profile latency at realistic request rates and choose optimizations accordingly.Read more
Some optimizations improve throughput but hurt single-request latency.
Batching, quantization, or graph compilation can introduce overhead that only pays off at scale. In low-traffic scenarios, this overhead dominates. Profile latency at realistic request rates and choose optimizations accordingly.
Common mistakes:
Optimizing without workload profiling
Using batch inference for real-time APIs
Ignoring cold-start costs
Optimize for your actual deployment context.
See lessHow do I detect silent label leakage during training?
Label leakage occurs when future or target information sneaks into input features. This often happens through timestamp misuse, aggregated features, or improperly joined datasets. The model appears highly accurate but fails in production. Audit features for causal validity and simulate prediction usRead more
Label leakage occurs when future or target information sneaks into input features.
This often happens through timestamp misuse, aggregated features, or improperly joined datasets. The model appears highly accurate but fails in production. Audit features for causal validity and simulate prediction using only information available at inference time.
Common mistakes:
Using post-event aggregates
Joining tables without time constraints
Trusting unusually high validation scores
If performance seems too good, investigate.
See lessWhy does my model’s accuracy fluctuate wildly between training runs?
Non-determinism is the usual culprit. Random initialization, data shuffling, parallelism, and GPU kernels all introduce variance. Without controlled seeds, results will differ. Set seeds across libraries and disable non-deterministic operations where possible. Expect some variance, but large swingsRead more
Non-determinism is the usual culprit.
Random initialization, data shuffling, parallelism, and GPU kernels all introduce variance. Without controlled seeds, results will differ.
Set seeds across libraries and disable non-deterministic operations where possible. Expect some variance, but large swings indicate instability.
Common mistakes:
Setting only one random seed
Comparing single-run results
Ignoring hardware differences
Reproducibility requires deliberate configuration
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 deployed model slowly become biased toward one class over time?
This usually happens when feedback loops in production reinforce certain predictions more than others. In many real systems, model outputs influence the data collected next. If one class is shown or acted upon more often, future training data becomes skewed toward that class. Over time, the model apRead more
This usually happens when feedback loops in production reinforce certain predictions more than others.
In many real systems, model outputs influence the data collected next. If one class is shown or acted upon more often, future training data becomes skewed toward that class. Over time, the model appears to “prefer” it, even if the original distribution was balanced.
To fix this, monitor class distributions in both predictions and incoming labels. Introduce sampling or reweighting during retraining so minority classes remain represented. In some systems, delaying or decoupling feedback from training helps break the loop.
Common mistakes:
Assuming bias only comes from training data. Retraining on production data without auditing it or monitoring accuracy but not class balance
Models don’t just learn from data — they learn from the systems around them.
See lessHow do I debug a transformer model that always predicts the same output?
When a transformer collapses to a single prediction, it’s almost always due to a training signal problem rather than model architecture. This happens if gradients are vanishing, labels are incorrectly encoded, or the loss function doesn’t match the task. For example, using CrossEntropyLoss with alreRead more
When a transformer collapses to a single prediction, it’s almost always due to a training signal problem rather than model architecture.
This happens if gradients are vanishing, labels are incorrectly encoded, or the loss function doesn’t match the task. For example, using
CrossEntropyLosswith already-softmaxed outputs will silently break learning.Start by checking that your labels vary and are correctly mapped. Then confirm that your final layer outputs raw logits and not probabilities. Run a single batch through the model and inspect gradient norms—if they’re near zero, learning isn’t happening.
Common mistakes:
Using the wrong loss for multi-class vs multi-label tasks
Forgetting to unfreeze pretrained layers
Training with a learning rate that’s too low to escape initialization bias
If predictions are identical after thousands of steps, stop training and validate your data pipeline before changing the model.
In fine-tuning scenarios, also confirm that layers aren’t frozen unintentionally. Many pretrained checkpoints load with frozen encoders by default.
See lessHow do I safely restore a WordPress backup without breaking the site?
Safe restores require matching the backup environment as closely as possible.Restoring files without the database, or vice versa, often causes mismatches. Always restore the database first, then files, then update wp-config.php. Afterward, regenerate permalinks and clear caches. Check for version miRead more
Safe restores require matching the backup environment as closely as possible.
Restoring files without the database, or vice versa, often causes mismatches.
Always restore the database first, then files, then update
wp-config.php. Afterward, regenerate permalinks and clear caches.Check for version mismatches, especially PHP and MySQL versions, which can cause silent failures.
The most common mistake is restoring backups directly onto live sites without testing.
See lessThe takeaway is to treat restores like migrations, not simple file uploads.