Field validation tests the results of machine learning model training against live data and can be used to evaluate the quality and consistency of predictions across different data sets (training, validation, and testing data).
Field validation ensures that the models optimized for predicting the outcomes using training data also produce quality, consistent predictions with a new set of data, called holdout data. Holdout data is typically set aside from training data for use during a different, separate time period. As a part of the model development and deployment life cycle, field validation is important to demonstrate to the business and process owners that the AI-based application can be embedded successfully into operations and produce dependable results.
C3.ai makes it easy to perform field validation as part of the C3 AI® ML Studio. The C3 AI® Suite is a complete, end-to-end platform for designing, developing, deploying, and operating enterprise AI applications at industrial scale. Machine learning services manage the full life cycle from experimentation to production. Experiments make it easy to partition existing data sets into training data, validation data, and testing data, and manage a continuous re-evaluation of models over time.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.