Data augmentation to optimise AI model training and prevent the model from overfitting

This service enhances the quality and usability of customer data. Before the service, customers may face challenges such as insufficient or non-uniform data, data privacy concerns, or a lack of properly labelled datasets. After the service, they receive enhanced datasets that are better normalised, more diverse, anonymised for privacy, and precisely labelled. This improvement facilitates better performance in AI models and analytics while ensuring compliance with privacy requirements. The augmentation methods are selected based on the customers needs and vary from case to case.

The service is delivered remotely. The process involves multiple iterations to refine data quality, applying techniques like normalisation, dimensionality reduction, noise addition, and labelling. The duration of the service depends on the dataset's size and complexity, generally taking a few days to weeks. Customers receive a detailed report and the augmented dataset ready for use in their AI or analytics workflows. Customers need to provide the raw datasets and specify their goals for augmentation.

Customization options include selecting specific preprocessing techniques, such as anonymization for privacy-sensitive data or targeted labelling for supervised learning. The service can be tailored to meet unique requirements, such as focusing on specific data patterns or ensuring compatibility with particular AI frameworks. Limitations may include the nature and quality of the input data and compatibility with the augmentation tools. Customers are encouraged to communicate any specific needs or constraints in advance.