Exploring Model Evaluation Methods
A trained deep learning model without any form of validation cannot be deployed to production. Production, in the context of the machine learning software domain, refers to the deployment and operation of a machine learning model in a live environment for actual consumption of its predictions. More broadly, model evaluation serves as a critical component in any deep learning project. Typically, a deep learning project will result in many models being built, and a final model will be chosen to serve in a production environment. A good model evaluation process for any project leads to the following:
- A better-performing final model through model comparisons and metrics
- Fewer production prediction mishaps by understanding common model pitfalls
- More closely aligned practitioner and final model behaviors through model insights
- A higher probability of project success through success metric evaluation
- A final model that is less biased...
