SGT for Training Models for Unlabeled Data
Enterprises must ensure they do not compromise potentially sensitive information when labeling their training data ML training models.
Stained Glass transforms unlabeled data for human labelling and model training while ensuring that unlabeled data remains confidential
Unlock data for training models and gain valuable insights
Hitachi, sought to label and train machine learning models using a car manufacturer’s data. However, the car manufacturer declined to share their sensitive manufacturing floor data, which created a challenge for Hitachi’s ML process that requires labeled data.
Hitachi implemented Stained Glass to anonymize data for human labelers. This approach enabled the car manufacturer to share only the information they deemed necessary. As a result, Hitachi was able to enable new business with the car manufacturer without compromising their sensitive data.
