TensorFlow and Deep Learning

This skill covers project structure, model development, training, evaluation, and deployment practices for neural networks built with TensorFlow and Keras.

Workflow for Training and Shipping a Model

1. Separate concerns into modules — Keep data loading, model definition, training, evaluation, and serving code in distinct files; treat notebooks as exploratory only.
2. Build the input pipeline first — Use tf.data.Dataset for scalable, prefetching input processing; validate shapes, dtypes, label ranges, and class balance before writing any model code.
3. Split before any fitting — Create train/validation/test splits before fitting normalization statistics or augmentation parameters, to avoid leakage.
4. Start with a tiny baseline — Build a small model and run a tiny overfit test (fit on a handful of examples until loss goes to ~0) to prove the training loop works before scaling up.
5. Train with callbacks — Wire in ModelCheckpoint (save best by validation metric, not final epoch), EarlyStopping, a learning-rate schedule, and TensorBoard logging.
6. Evaluate with task-appropriate metrics — Use a held-out test set only for final reporting, never for tuning.
7. Export with a explicit signature — Save with model.export() or tf.saved_model.save() and define serving input signatures explicitly.
8. Smoke test the export — Load the exported model and run inference on sample inputs before deploying.

Project Structure