moai-lang-python

FastAPI with type safety involves using Python type hints, async patterns, and comprehensive test coverage with pytest. This Skill guides you through setting up FastAPI applications with mypy for type checking, ruff for linting, and pytest for 95%+ test coverage, ensuring production-grade code quality and reliability.

AI/ML integration in Django or Flask requires careful dependency management, deployment strategies, and performance optimization. This Skill covers deploying ML models within modern Python frameworks, handling inference pipelines, managing dependencies with uv, and optimizing for production environments across cloud platforms.

Async database operations use async/await patterns with frameworks like FastAPI and tools like SQLAlchemy async drivers. This Skill teaches you to combine async patterns with mypy type checking, ruff linting, and pytest testing to validate data operations, catch errors early, and maintain code quality throughout development.

Yes. pytest and mypy work together to validate data science code using Pandas, NumPy, and Polars. This Skill shows you how to structure testing for data transformations, apply type hints to data operations, and catch type errors before runtime, ensuring reliable data pipelines and reproducible results.

Cloud deployment security involves dependency management with uv, containerization with Docker, orchestration with Kubernetes, and CI/CD pipeline security. This Skill covers best practices for securing FastAPI, Django, and Flask applications in production, including authentication, secrets management, and secure deployment workflows.

Docker and Kubernetes are industry standards for containerized Python deployments but add complexity. This Skill teaches when to use Docker containers and Kubernetes orchestration versus simpler deployment approaches, helping you choose the right deployment strategy based on scale, infrastructure, and operational requirements.