Apache Airflow is an open-source platform for authoring, scheduling, and monitoring complex data workflows. In machine learning, pipelines rarely consist of a single step — data must be ingested, cleaned, transformed, features engineered, models trained, evaluated, and deployed in a specific order, with each step depending on the previous one. Airflow lets ML engineers define these multi-step pipelines as code using Python, providing visibility, retry logic, scheduling, and dependency management out of the box. Rather than stitching together cron jobs or fragile shell scripts, teams use Airflow to build reliable, reproducible, and auditable ML pipelines that can be monitored from a central web UI.
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.