Flask
Flask is a lightweight Python web framework for building HTTP applications and APIs. In AI projects, competence means constructing clear request handlers and application structure while supplying validation, authentication, deployment and resource management that a minimal framework deliberately leaves to the application.
What it is
Flask uses the WSGI web application interface and provides routing, request and response objects, configuration and template integration. Applications can organize related behavior into blueprints and create configured instances through an application factory. Its minimal core differs from FastAPI's type-driven validation and ASGI-centered design. A Flask route can invoke a model, but the framework does not determine model batching, inference capacity or background job execution. Application and request contexts govern access to relevant objects, so resource ownership and lifecycle must be understood when code runs outside a request.
What the work involves
The practitioner separates HTTP handling from model logic, validates external inputs explicitly and defines predictable errors. They organize configuration and dependencies for development and deployment, choose a production server and keep expensive model initialization out of per-request work. Authentication and request-size limits are integrated according to the application. Tests exercise handlers and the important service boundaries. Useful work delivers a small, maintainable web service with known behavior under invalid input and concurrent use, rather than a development route that only handles a demo.
Illustrative example
An engineer exposes a fraud-scoring model to an internal application. A Flask blueprint validates a shipment schema, calls a separately tested scoring function and returns a versioned response. The model loads during application setup. Tests check missing fields and model errors, while a deployment exercise verifies memory usage across server workers and ensures diagnostic responses do not reveal customer records.
Limits and common mistakes
The built-in development server is not deployment evidence, and adding async syntax does not remove WSGI execution constraints. Minimal validation can permit malformed data to reach the estimator; careless global state can mix request-specific information. Additional extensions have their own compatibility and security requirements. Check request context, worker configuration and failure responses. Flask's simplicity can help a small service, but required application responsibilities remain real work.
Prerequisites
Related skills
- → is an instance of: API Development
Sources and further reading
- Flask documentation
Supports WSGI architecture, routing, application factories, contexts, testing and production deployment.
Last updated: 2026-10-10