A data drift detection and schema validation package
Project description
About The Package
The package is a wrapper of tensorflow data validation for our specific needs. It can analyze training data and serving data to compute desscriptive statistics, infer a schema, and detect anomalies.
Dependencies
Installation
pip install data-drift-detector
Usage
Initialize a Harvest client:
# The Dataset, TrainDataset, ServeDataset can be initialized with different methods.
train = TrainDataset.from_GCS()
train = TrainDataset.from_bigquery()
train = TrainDataset.from_dataframe()
train = TrainDataset.from_stats_file()
Populate the class variables and submit.
# Get training dataset schema
schema = train.schema_dict()
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