Training Pipeline: From Pretraining to Production

The Training Pipeline

Training a classifier at scale requires careful orchestration of data, compute, and validation. The pipeline typically spans weeks of experimentation before any model reaches production.

Pretraining Foundation

Most production classifiers start from a pretrained backbone like ResNet, EfficientNet, or Vision Transformer. These models learned general visual features from millions of images. Pretraining provides a massive head start: instead of learning edges and textures from scratch, your model already understands visual primitives.

Why this matters: Training from scratch on 1 million images might achieve 70% accuracy. Fine-tuning a pretrained model on the same data reaches 90%+ because the early layers already extract useful features.

Fine-tuning Strategy

Learning rate schedule: Start low (1e-5 to 1e-4) for pretrained layers to avoid destroying learned features. Use higher rates for new classification layers. Gradually unfreeze deeper layers as training progresses.

Data augmentation: Random crops, flips, color jitter, and mixup expand your effective dataset size 10-100x. Without augmentation, models memorize training images rather than learning generalizable features.

Validation strategy: Hold out 10-20% of data that never touches training. Monitor validation loss to catch overfitting. If validation loss rises while training loss falls, stop training.

Distributed Training

Large datasets require multiple GPUs. Data parallelism splits batches across GPUs: each GPU computes gradients on its portion, then gradients are averaged. With 8 GPUs, you can train on 8x larger batches or finish 8x faster. Communication overhead limits scaling beyond 32-64 GPUs for most workloads.