Why this matters: In data engineering, Python environments often require heavy numerical and machine learning libraries that take minutes to download and install. BuildKit cache mounts persist these downloaded packages across builds, transforming sluggish container image creation into a cache-efficient process that significantly reduces CI/CD wait times.

Consider a hypothetical pipeline that processes sensor data using a heavy stack of libraries: pandas , scikit-learn , numpy , and pyarrow . Every time a developer pushes a code change to the repository, the CI/CD runner triggers a fresh Docker build. Because the pip install layer clears its internal cache after every run, the runner re-downloads the same multi-megabyte wheels from PyPI on every single commit.

Symptoms

Engineers often notice this performance bottleneck during the "Build" phase of a CI pipeline.…