Aug 16, 2026 · 1 min read
End-to-end MLOps for vision-based anomaly detection from labeling to model monitoring.
This article demonstrates dataset balancing, augmentation, and confidence calibration for production reliability.
train_loader = DataLoader(train_set, batch_size=32, shuffle=True, num_workers=4)
for images, labels in train_loader:
loss = criterion(model(images), labels)L = -\sum_{i=1}^{N} y_i log(\hat{y}_i)
Schema contracts, drift checks, and online/offline parity safeguards.
Latent-space voting and reconstruction thresholds for early fault alerts.
Forecasting pre-failure patterns from connected sensor graphs.