Key Aspects of our Data Curation Services
▪ Data Collection: Gathering relevant, diverse, and representative data sources is the initial step. This ensures a robust foundation for model training.
▪ Data Cleaning: Removing noise, handling missing values, and rectifying inconsistencies are essential for accurate model performance.
▪ Feature Engineering: Selecting, transforming, and creating features enhances the model’s ability to extract meaningful patterns from the data.
▪ Labeling and Annotation: In supervised learning, accurate and consistent labeling is crucial. This may involve human-in-the-loop processes or automated approaches.
▪Data Balancing: Ensuring each class or category has sufficient representation prevents model bias towards dominant classes.
▪ Data Versioning and Tracking: Keeping track of changes, versions, and sources of data is crucial for reproducibility and accountability.
▪Privacy and Compliance: Adhering to data privacy regulations and best practices is non-negotiable