Problem and data
Assess data volume, label consistency and error costs. Deep learning is compared with simpler methods rather than assumed suitable for every task.
Development and integration
Select architecture and transfer learning for classification, detection or segmentation. Data rights and model licences are assessed for the project.
Evaluation and deployment
Evaluate overfitting, per-class errors and generalisation on separate tests. Inference cost, versioning and rollback belong to deployment design.