FastAPI

Load model into memory on startup.

Create a POST endpoint to receive tensor data.

Validate input using Pydantic models.

Run async inference using the model backend.

Return structured JSON predictions.

Initialize a WebSocket endpoint.

Authenticate user via JWT in the query string.

Use an async loop to fetch data from Redis.

Broadcast data to connected clients.

Handle disconnection gracefully.

Define shared schemas as Pydantic models.

Use FastAPI's APIRouter to modularize services.

Implement cross-service auth using OAuth2.

Deploy each service in a Docker container.

Monitor using the /metrics endpoint (Prometheus).

Set up an endpoint to receive batches of data.

Use Pydantic to filter out malformed entries.

Perform async transformations on valid records.

Bulk insert into a PostgreSQL database.

Return status report of processed items.

Use Mangum to wrap the FastAPI app for AWS Lambda.

Define clear request/response models for mobile devs.

Utilize FastAPI's dependency injection for DB access.

Deploy using Terraform or AWS SAM.

Monitor execution times via CloudWatch.

Use Jinja2Templates to serve HTML pages.

Create forms that map to FastAPI endpoints.

Use BackgroundTasks for long-running scripts.

Secure the tool behind SSO.

Provide a 'Try it out' doc page for other team members.

Integrate Strawberry or Ariadne with FastAPI.

Define GraphQL types based on Python classes.

Implement resolvers that call other REST services.

Mount the GraphQL app on a FastAPI route.

Use GraphiQL for interactive querying.