Insufficient data causing
Privacy preventing access to key training data?
STADLE increases model performance by solving your training data problems.
85% of AI projects will deliver erroneous outcomes due to bias in data - Gartner
Model bias due to lack of a wider data from human bias or data collection
ML models learning from noise and inaccuracies as the data set is too large
Making wrong predictions due to high bias and low variance with small dataset
Data collection issues from all sources due to privacy and other restrictions
Training on irrelevant low quality data leading to model issues
Insufficient values in a data set impacting ML model performance
Unable to access
crucial data due to the risk in data security
on data transfer and
storage for ML
How we help?
We assess your AI models for underfitting and overfitting to identify training data gaps
We build federated training models and use STADLE to collaboratively train your AI models for better performance
We teach you on how you can use STADLE to address your training data gaps