Improve Machine Learning Performance with Synthetic Data
Boost machine learning (ML) training datasets with datadoo's synthetic data platform to improve model performance and ensure data privacy and security across the MLOps lifecycle.
Data Quality
Issues with data quality such as missing fields and unwanted bias greatly impact model performance, jeopardizing the utility of models in production.
Data Availability
Training models requires large amounts of cleaned, curated, and annotated data. Collecting ground-truth data is time-consuming and expensive.
Data Privacy
ML teams need access to sensitive data to train, evaluate, and improve models. Provisioning access to data takes months and raises compliance concerns.
The ML training challenge
To unlock the value of machine learning models, organizations must train them on domain-specific proprietary data, enabling them to excel in specialized tasks. This is the most challenging task for machine learning teams.
Referred to as the ‘data bottleneck’, the problem addresses the inability of organizations to rapidly extract value from AI due to challenges pertaining to training data availability, data quality, or data privacy.
As a result, ML projects often fail to take flight, remain confined in innovation labs, and never reach production.
The ML training data solution
datadoo empowers organizations to accelerate the last mile of ML training via safe access to synthetic data. datadoo synthetic data platform provides the end-to-end capabilities for generating, evaluating, and operationalizing synthetic data to improve ML robustness and performance. This includes advanced anonymization of sensitive entities with mathematical privacy guarantees, augmentation of whole datasets, boosting limited classes, and even simulation of rare edge cases.
Key Benefits
