As more users and integrations modify data, enforcement weakens. Validation rules may be bypassed or incomplete. Business meaning evolves faster than enforcement mechanisms. Ongoing governance is required.Takeaway: Data quality is a continuous process, not a one-time setup.
As more users and integrations modify data, enforcement weakens. Validation rules may be bypassed or incomplete.
Business meaning evolves faster than enforcement mechanisms.
Ongoing governance is required.
Takeaway: Data quality is a continuous process, not a one-time setup.
Why does my retrained model perform worse on old data?
This is a classic case of catastrophic forgetting. When retraining only on recent data, the model adapts to new patterns while losing performance on older distributions. This is common in incremental learning setups. To fix it, mix a representative sample of historical data into retraining or use reRead more
This is a classic case of catastrophic forgetting.
When retraining only on recent data, the model adapts to new patterns while losing performance on older distributions. This is common in incremental learning setups.
To fix it, mix a representative sample of historical data into retraining or use rehearsal techniques. Regularization toward previous weights can also help.
Common mistakes:
Training only on the latest data window
Assuming more recent data is always better
Dropping legacy edge cases
Retraining should expand knowledge, not replace it.
See lessWhat causes NaN losses during model training?
NaNs usually come from invalid numerical operations. Common sources include division by zero, log of zero, exploding gradients, or invalid input values. In deep models, this often appears after a few unstable updates. Start by enabling gradient clipping and lowering the learning rate. Then check youRead more
NaNs usually come from invalid numerical operations.
Common sources include division by zero, log of zero, exploding gradients, or invalid input values. In deep models, this often appears after a few unstable updates.
Start by enabling gradient clipping and lowering the learning rate. Then check your input data for NaNs or infinities before it enters the model.
If using mixed precision, confirm loss scaling is enabled correctly.
Common mistakes:
Normalizing with zero variance features
Ignoring data validation
Training with unchecked custom loss functions
NaNs are symptoms—fix the instability, not the symptom.
See lessWhy does my model pass offline tests but fail A/B experiments?
Offline metrics often fail to capture real user behavior. In production, user interactions introduce feedback loops, latency constraints, and distribution shifts that static datasets don’t reflect. A model may optimize for offline accuracy but degrade user experience. Instrument live metrics and anaRead more
Offline metrics often fail to capture real user behavior.
In production, user interactions introduce feedback loops, latency constraints, and distribution shifts that static datasets don’t reflect. A model may optimize for offline accuracy but degrade user experience.
Instrument live metrics and analyze segment-level performance. Often the failure is localized to specific cohorts or edge cases.
Common mistakes:
Relying on a single offline metric
Ignoring latency and timeouts
Deploying without gradual rollout
Offline success is necessary but never sufficient.
See lessHow can prompt length cause unexpected truncation?
LLMs have strict context length limits. If system messages, instructions, and user input exceed this limit, earlier tokens are dropped silently. This often removes critical instructions. Always calculate token usage explicitly and reserve space for the response. Truncate user input, not system prompRead more
LLMs have strict context length limits.
If system messages, instructions, and user input exceed this limit, earlier tokens are dropped silently. This often removes critical instructions.
Always calculate token usage explicitly and reserve space for the response. Truncate user input, not system prompts.
Common mistakes:
Assuming character count equals token count
Appending logs or history blindly
Ignoring model-specific context limits
Context budgeting is essential for reliable prompting.
See lessWhy 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 debug incorrect token alignment in transformer outputs?
Token misalignment usually comes from mismatched tokenizers or improper handling of special tokens. This happens when training and inference use different tokenizer versions or settings. Even a changed vocabulary order can shift outputs. Always load the tokenizer from the same checkpoint as the modeRead more
Token misalignment usually comes from mismatched tokenizers or improper handling of special tokens.
This happens when training and inference use different tokenizer versions or settings. Even a changed vocabulary order can shift outputs.
Always load the tokenizer from the same checkpoint as the model. When post-processing outputs, account for padding, start, and end tokens explicitly.
Common mistakes:
Rebuilding tokenizers manually
Ignoring attention masks
Mixing fast and slow tokenizer variants
Tokenizer consistency is non-negotiable in transformer pipelines.
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 less