Start with a focused hypothesis: e.g., "A small fine-tuned transformer (or ResNet) can reach acceptable accuracy for task X with ~100 labeled examples via transfer learning + active learning."Workflow (lightweight, ~1–2 weeks prototyping):1. Setup & tooling- Labeling: Label Studio or Prodigy for fast human-in-the-loop labeling.- Training/experiment tracking: PyTorch or scikit-learn + Weights & Biases.- Orchestration: local Docker or a single GPU on SageMaker / GCP AI Platform.- Versioning: DVC or simple Git + dataset snapshots.2. Data strategy & sample-size- Seed set: label 50–200 high-quality examples stratified by class (if multiclass).- Use transfer learning / pretrained models to reduce data need.- Apply data augmentation and lightweight synthetic generation if appropriate.- Expect diminishing returns: often big gains up to ~200–500 examples; tune per problem.3. Rapid loop (Active Learning + few-shot transfer)- Train a small model on seed set (few epochs).- Use uncertainty sampling (entropy or margin) on unlabeled pool to pick next K (10–50) samples to label.- Iterate 5–10 cycles or until metric plateaus.Example active-learning loop:python
# sketch: uncertainty sampling with sklearn/pytorch
for cycle in range(max_cycles):
model.train(seed_loader)
probs = model.predict_proba(unlabeled_X)
uncertainty = -np.max(probs, axis=1) # higher = more uncertain
idx = np.argsort(-uncertainty)[:batch_size]
new_labels = human_label(unlabeled_X[idx])
add_to_seed(new_labels)
if validation_metric_improved_less_than(threshold): break
