Hi, I think the accepted answer is pointing in the right direction.

Given that DBR 17.3 is an LTS runtime, this is more likely to be a packaging or deployment issue than a runtime regression.

One common cause is wheel caching. If your wheel version doesn't change between deployments, pip running on the cluster may determine that the package is already installed and skip reinstalling it. That can produce exactly the kind of intermittent behavior you're seeing.

If you're using Databricks Asset Bundles, consider enabling dynamic_version:

artifacts:
  my_project:
    type: whl
    build: python setup.py bdist_wheel
    dynamic_version: true

This feature (introduced in Databricks CLI 0.245.0) appends a unique timestamp to each generated wheel version so every deployment is treated as a new package and reinstalled accordingly.

It's also a good idea to clean the dist/ directory before each build. If your artifact path uses a wildcard, an older wheel remaining alongside the newly built one is another common source of inconsistent deployments.