ML Model Development
Choosing the training path
SageMaker offers built-in algorithms (XGBoost, linear learner, k-means, DeepAR, BlazingText), framework containers (PyTorch, TensorFlow, scikit-learn), and custom containers. Built-ins are fastest to production and support distributed training; script mode lets you bring existing code with minimal changes. Traps: picking a custom container when a built-in fits, or forgetting that built-in algorithms expect specific input formats such as RecordIO-WebDataset or CSV with the label in column zero.
Tuning and experiments
Automatic Model Tuning runs Bayesian or random search over hyperparameter ranges, with early stopping to kill poor trials. SageMaker Experiments tracks runs, parameters, and metrics for reproducibility. Trap: tuning on the test set. Always tune against a validation split and hold the test set for a single final estimate.
Evaluation discipline
Match the metric to the business cost: precision/recall trade-offs, F1 for balance, AUC for ranking, MAE/RMSE for regression, and custom metrics for imbalanced or cost-sensitive problems. Use SageMaker Debugger for vanishing gradients, overfitting, and stalled training, and SageMaker Model Registry to version model packages with approval status.
Worked example
For a fraud model with 0.5% positives, rule of thumb: optimize recall at a fixed precision floor rather than accuracy. Train XGBoost in script mode, tune with a custom metric logged to the tuner, select the threshold on validation data, and register the chosen model package with metrics attached for audit.
Reproducibility
Pin container image URIs, record data versions, set seeds, and capture the training job's hyperparameters so any registered model can be retrained.