ML model broken in production.Fixed this session.

Accuracy degradation without a code change is almost always data drift or a feature pipeline breaking silently. Root causes: the upstream data source changed its schema or encoding without notice, a feature transformation step started producing NaN or zero values due to a null in a new input field, training data and serving data are now coming from different time windows causing distribution shift, or a categorical encoder was fitted on training categories and is now receiving unseen categories in production (returning -1 or encoding incorrectly). The engineer compares the feature distribution between training and current serving data using KL divergence or PSI scores, identifies the drifted feature, and either retrains the model on recent data or patches the preprocessing pipeline to handle the new input distribution.

CUDA OOM during training is caused by: batch size that was borderline and now OOMs after a model architecture change, gradient accumulation not being used when training with larger inputs, a memory leak where intermediate tensors are not being freed because .detach() or del is missing in the training loop, or DataLoader workers loading the full dataset into shared memory. The engineer profiles GPU memory using torch.cuda.memory_summary() and nvidia-smi --query-compute-apps, identifies the retained tensor graph, and applies: gradient checkpointing to trade compute for memory, mixed precision training with torch.cuda.amp.autocast(), reduced batch size with gradient accumulation to maintain effective batch size, or pin_memory=False in the DataLoader to reduce shared memory pressure.

Inference latency spikes at scale are caused by: model serving without batching - each request triggering a separate forward pass on a GPU that performs best with batch inputs, the model running on CPU instead of GPU due to a misconfigured serving container, a feature store lookup adding a synchronous database round-trip per request, or the model not being compiled with torch.compile() or converted to TorchScript/ONNX for production serving. The engineer profiles the latency breakdown using a distributed trace or manual timing context managers, identifies the bottleneck (model forward pass vs feature retrieval vs serialization), and applies dynamic batching in TorchServe or Triton, converts the model to ONNX with quantization, or moves synchronous feature lookups to an async batch prefetch pattern.

Training-serving skew is one of the most common causes of silently degraded ML model performance. It occurs when: the feature engineering code was duplicated between the training notebook and the serving pipeline instead of using a shared library, the feature store is serving cached values from a different time window than training used, a feature normalization scaler was fitted on training data and the fitted parameters were not saved - the serving pipeline refits on live data, or timezone handling differs between the training data pull (UTC) and the serving timestamp (local time). The engineer writes a point-in-time feature consistency test that runs the same input through both pipelines and asserts value equality, then traces any divergence to the root transformation step.

Every QuickHire ML engineer passes: a live debugging exercise on a broken PyTorch training pipeline with a hidden gradient accumulation bug and a feature skew problem, a written assessment covering model evaluation metrics (precision-recall tradeoffs, AUC-ROC interpretation), PyTorch internals (autograd, custom backward passes, DataLoader collate_fn), MLOps tooling (MLflow experiment tracking, model registry, Feast or Tecton feature stores), and model serving patterns (batching, quantization, TorchServe vs Triton), plus a portfolio review of production ML systems. Only the top 3% pass. PM matches engineers to your stack - framework (PyTorch vs TensorFlow), serving infrastructure (SageMaker, Vertex AI, Triton, custom FastAPI), and MLOps platform (MLflow, Weights and Biases, Kubeflow).

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