Offers a comprehensive guide to Python wheels, emphasizing their crucial role in modern machine learning (ML) workflows.

It explains that wheels are pre-built, ready-to-install package formats that offer significant advantages over source distributions, including faster installation, improved reliability, and enhanced security, especially vital for ML libraries with compiled code.

The sources detail the anatomy of a wheel, from its compatibility-defining filename to its internal structure and metadata, highlighting the importance of pyproject.toml for declarative project configuration.

Furthermore, the text covers the wheel creation workflow, the challenges of cross-platform compatibility (especially for Linux with manylinux and auditwheel), and how to optimize packages using tools like Cython and Numba.

Finally, it illustrates how CI/CD pipelines automate wheel building and deployment, examining real-world applications in major ML frameworks and companies, and discussing future trends in Python packaging aimed at addressing issues like GPU-specific builds and dependency resolution.