Data Validation for Improving & Tuning ML Model Accuracy
Checking the accuracy of the labeled datasets is often examined twice: prior to training the Machine Learning model in order to validate that the model will be trained on high quality annotations and the post model results in order to validate if the model predicted correctly.
Human-in-the-loop review to validate the quality of the datasets pre and post model is essential for almost every AI development. Most of the time, you’d just need a quick glance and verdict from a human (or several, to create higher confidence scores) to validate that the data is annotated accurately. Tasq.ai can generate millions of human validations faster and more cost effectively than any other solution and can be deployed seamlessly in the production process of your AI developments.