High-quality training data is the vital foundation that enables the successful functioning of AutoQL as a whole system. Typically, generating training data is a monumental and highly manual task. When it comes to AutoQL, we've semi-automated this process, which means we take on the heavy lifting and we get the job done quickly.
- Training Data - An initial set of data used to help a program understand how to apply technologies like neural networks to learn and produce sophisticated results.
Once generated, training data essentially serves as a curriculum for the AI, enabling all of the machine learning (ML) models that make up the AutoQL system to learn and understand your unique database and work together to consistently understand query inputs, return accurate responses, and provide your users with an exceptional experience.
For scalability, new training methods utilize advanced text scraping to rapidly generate customized training corpuses on professional jargons – so that end users can communicate using the familiar language of their industry or business and feel confident that they are getting solid, relevant database content instantly – all from within their respective software application interfaces.
- Training Corpus - The training corpus represents a dataset that is used to train a model.
Updated almost 3 years ago