Learn how to create a web-based terminal interface that streams a pseudo-terminal (PTY) over WebSockets using Flask-SocketIO, enabling users to interact with command-line applications like Mininet directly from their browsers without any installation.
A deep dive into the acronym sweep engine of STORM DAT, a Python-based document analysis tool that uses regex and state machines to enforce complex compliance rules across multi-page Word documents.
DuckDB is an in-process analytical database that runs inside your Python application, reads Parquet files natively, and handles complex SQL including window functions, QUALIFY, and MEDIAN — with no server to manage. This tutorial covers the read-only connection pattern used in the flight disruption serving layer, thread-safety in a FastAPI threadpool, and the queries that power the reliability API.
FastAPI's type annotations, Pydantic models, and lifespan context manager solve three common ML serving problems: loading expensive artifacts before requests start, validating inputs without manual checks, and returning useful errors when dependencies aren't ready. This tutorial walks through the patterns used in the flight disruption prediction API.
An ML inference API has a constraint that a standard REST API does not: its most expensive resource is not a database connection or a network socket — it is the model artifact itself.
dbt turns SQL SELECT statements into tested, documented, dependency-aware data models. This tutorial builds the flight reliability marts used by the dashboard — airport, route, and carrier delay rates — starting from a staging view and layering on not-null, uniqueness, relationship, and range tests that catch data quality problems before they reach the API.
