  Data corruption is an impediment to modern machine learning deployments.
Corrupted data can severely bias the learned model and can also lead to invalid
inferences. We present, Picket, a simple framework to safeguard against data
corruptions during both training and deployment of machine learning models over
tabular data. For the training stage, Picket identifies and removes corrupted
data points from the training data to avoid obtaining a biased model. For the
deployment stage, Picket flags, in an online manner, corrupted query points to
a trained machine learning model that due to noise will result in incorrect
predictions. To detect corrupted data, Picket uses a self-supervised deep
learning model for mixed-type tabular data, which we call PicketNet. To
minimize the burden of deployment, learning a PicketNet model does not require
any human-labeled data. Picket is designed as a plugin that can increase the
robustness of any machine learning pipeline. We evaluate Picket on a diverse
array of real-world data considering different corruption models that include
systematic and adversarial noise during both training and testing. We show that
Picket consistently safeguards against corrupted data during both training and
deployment of various models ranging from SVMs to neural networks, beating a
diverse array of competing methods that span from data quality validation
models to robust outlier-detection models.
