In AI product development, your model is only as good as the data that powers it—and that data is only as valuable as your ability to manage, govern, and scale it effectively. Data operations (DataOps) and governance represent the critical infrastructure that separates AI products that scale successfully from those that collapse under technical debt, compliance violations, or quality degradation.
123456789101112import great_expectations as gx from great_expectations.core.batch import BatchRequest from datetime import datetime # Initialize Great Expectations context context = gx.get_context() # Define data quality expectations for training data expectation_suite = context.add_expectation_suite( expectation_suite_name="training_data_quality" )
123456789101112from dataclasses import dataclass from typing import Dict, List, Optional import pandas as pd from datetime import datetime, timedelta @dataclass class DataQualityCheck: name: str severity: str # 'critical', 'warning', 'info' threshold: float @dataclass
123456789101112# dvc.yaml - Pipeline definition with data versioning stages: prepare_data: cmd: python src/prepare.py deps: - src/prepare.py - data/raw/ params: - prepare.split_ratio - prepare.random_seed outs: - data/processed/train.parquet