AI and Deep Learning Workflow Fundamentals
The end-to-end lifecycle
A typical workflow: problem framing, data collection and labeling, preprocessing and augmentation, model selection, training, evaluation, hyperparameter tuning, and deployment with monitoring. Questions often test where a step belongs. Data splitting happens before training; the test set is touched once at the end; validation guides tuning.
Training mechanics
Understand forward pass, loss computation, backpropagation, and optimizer updates. Batch size interacts with learning rate; gradient accumulation simulates larger batches under memory limits. Mixed precision (FP16/BF16 with loss scaling) speeds training while preserving accuracy. A frequent trap is confusing epochs (full passes over data) with iterations (weight updates), especially when gradient accumulation is used.
Overfitting and regularization
Recognize signs: training loss falls while validation loss rises. Remedies include more data, augmentation, dropout, weight decay, and early stopping. A trap is assuming a lower training loss means a better model.
Transfer learning and evaluation
Pretrained models are fine-tuned by freezing early layers and training later ones. Choose metrics that match the problem: accuracy is misleading on imbalanced classes, where precision, recall, F1, or AUC are better.
Worked example
Suppose validation accuracy plateaus while training accuracy reaches 99%. That is overfitting, not underfitting. First response: add augmentation or regularization and check the data pipeline for leakage, rather than increasing model capacity.
Data quality
Class imbalance, label noise, and distribution shift are common failure sources. Rule of thumb: inspect a sample of your data before tuning hyperparameters, because data problems masquerade as model problems.