What is data validation?
Building successful artificial intelligence (AI) systems that produce viable outcomes for the real world requires large volumes of diverse and accurately labeled datasets. A high-quality dataset is one that has undergone a rigorous cleaning process and that contains all of the necessary parameters for a machine learning (ML) model to do its task.
Data validation is a term used to describe the process of checking the accuracy and quality of source data to ensure accurate output. Implementing data validation processes for a ML model helps to mitigate “garbage in = garbage out” scenarios, where poor quality data produces a poorly functioning model.
Once a ML model has been fed training data, validation data is used to determine whether the model can correctly identify new data or if it’s overfitting the original dataset. Overfitting occurs when the model memorizes the “noise” in the training dataset and is unable to perform the tasks it was intended for with new data.
Benefits of data validation
Effective data validation practices have a number of benefits including:
- Improved model accuracy: Validation enables data scientists to adjust hyperparameters and improve accuracy.
- Early detection of errors: It is common that predictions generated by ML models are used to generate more training data. Because of this, a small data error could have larger implications to a model’s performance over time. Robust data validation has the ability to identify errors early, before they cause more significant issues.
- Reduced costs: The early detection of errors reduces the total number of hours engineers need to work on a model, saving time and money.
- Higher quality customer experiences: Data validation ensures models are developed for success in real world applications. Accurate models produce high-functioning products and services that provide top-notch customer experiences.