Define Problem and Metrics
1-2 daysFrame the business problem, select evaluation metrics, and define success criteria.
Field context
This workflow is part of 2 niche fields
Complete guide for ml model training pipeline — step-by-step workflow, tools, checklist, and expert tips to get started.
Frame the business problem, select evaluation metrics, and define success criteria.
Calculate train/validation/test splits and cross-validation fold configuration.
Engineer features, train models, and tune hyperparameters on validation data.
Run final evaluation on held-out test set and document results for deployment.
Configure train/validation/test split ratios for the pipeline.
Determine optimal k-fold count based on dataset size.
Estimate training duration for resource planning during model development.
Verify GPU memory requirements before scaling up model complexity.
Key benchmarks for ml model training pipeline.
| Set | Typical % | Purpose |
|---|---|---|
| Train | 70-80% | Fit model |
| Validation | 10-15% | Tune hyperparams |
| Test | 10-15% | Final eval only |
Ship a simple baseline before complex models — it validates the pipeline and sets a performance floor.
Track dataset versions, code commits, and model artifacts together — reproducibility depends on all three.
Use cloud spot/preemptible instances for training — 60-90% cheaper with checkpointing for interruptions.