Great Expectations (GE) is an open-source Python library that brings automated data quality validation to ML pipelines. In production systems, data quality issues are among the leading causes of model degradation — a model trained on clean data may produce garbage predictions when fed malformed or drifted inputs. Great Expectations addresses this by letting you define explicit, testable assertions (called expectations) about your data's structure, completeness, and statistical properties. These assertions run automatically at pipeline ingestion points, catch anomalies early, generate human-readable validation reports, and integrate natively with orchestrators like Airflow and Prefect.
Analogy🏏Cricket
🏏 Think of it like cricket: Evidently AI is the IPL's official analytics platform — rather than each franchise building their own stats system, they use a shared platform that automatically computes every standardized metric: batting averages, economy rates, strike rates, net run rates. When Virat Kohli's performance drifts from his baseline, the platform highlights it automatically with charts. Evidently does the same for ML models: instead of each team coding their own drift detectors, they use Evidently's pre-built metrics and get standardized, comparable reports automatically. The standardization is the strategic point, not a convenience: because every franchise reads the same metric definitions, a drift score of 0.3 means the same thing in every dashboard, reports can be compared across teams and seasons, and a new analyst is productive on day one. Hand-rolled monitoring scripts fail exactly here — every team's 'drift check' quietly means something different, and nobody can audit whose alarm was right.