Deep Learning Production Guide: Training Stability, Scale, and Deployment

Deep learning production interviews test whether you can train and ship neural models reliably—not just cite backprop equations. Interviewers ask about vanishing gradients, learning rate schedules, batch norm placement, and how you debug NaN losses under mixed precision.

Architectural choices—CNNs for vision, RNNs for sequences, transformers for general domains—should map to data and SLA requirements. Candidates explain when fine-tuning a foundation model dominates training from scratch, and how to estimate GPU budgets realistically.

Distributed training introduces synchronization overhead, gradient accumulation strategies, and checkpointing for fault tolerance. Even inference-focused roles benefit from understanding data parallel basics when teams fine-tune open models internally.

Deployment concerns include ONNX export, TensorRT optimization, dynamic batching, and GPU memory fragmentation. Discuss warm-up requests, model server choices, and A/B tests that compare latency and accuracy after graph optimizations.

Monitoring deep models in production means tracking input drift, confidence distributions, and out-of-distribution detection. When performance drops, articulate whether to retrain, adjust thresholds, or fall back to simpler baselines while investigating.