9 docs tagged with "production"

How AI is deployed across industries - real architectures, domain constraints, and production patterns from finance, healthcare, legal, retail, manufacturing, and education.

Sentry integration, custom error grouping, breadcrumbs, release tracking, and building production error workflows in Python.

Dependency injection, lifespan events, background tasks, middleware, custom exception handlers, OpenAPI customisation, and production FastAPI patterns.

Liveness vs readiness probes, dependency health checks, health check libraries, SLOs, and building production-grade health endpoints in Python.

Profiling, Cython, Numba, memory optimisation, async performance, and Python at scale - turning Python code from slow to production-fast.

Structured logging, metrics, distributed tracing, error tracking, and health checks - the three pillars of production observability in Python.

Run, fine-tune, quantize, evaluate, and deploy open source LLMs in production - the complete hands-on guide for engineers who want to own their models.

Object memory overhead, __slots__, generators, memory-mapped files, and GC tuning - reducing Python's memory footprint in production.

Python logging module internals, structlog, JSON logs, correlation IDs, log levels, and log aggregation - from print() to production-grade structured logs.