Model Validation refers to the process of evaluating a machine learning model’s performance on a separate dataset (often called the validation set) to ensure it generalizes well to new, unseen data. This step is crucial for tuning model parameters, selecting the best model, and preventing overfitting. Validation helps in assessing how well the model will perform in real-world scenarios.
Model Observability, on the other hand, involves monitoring and understanding the model’s performance and behavior in production over time. It includes tracking metrics, detecting Performance Drift, and ensuring the model continues to function as expected in dynamic environments.
While model validation is a step in the model development process, model observability is an ongoing practice once the model is deployed. Both are related in that they aim to ensure the model’s reliability and effectiveness, but they occur at different stages of the model lifecycle. Validation is about initial performance assessment, whereas observability is about continuous monitoring and maintenance.