FastAPI is a modern, high-performance Python web framework built on top of Starlette and Pydantic. It is designed specifically for building APIs quickly and with minimal boilerplate. For ML engineers, FastAPI is a natural fit for serving trained models as REST endpoints because it provides automatic OpenAPI documentation, built-in data validation via Pydantic, async support for high-throughput workloads, and lightning-fast performance comparable to Node.js and Go. Instead of writing serializers, validators, and documentation manually, FastAPI generates all of these from standard Python type hints, letting you focus on model logic rather than plumbing.
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.