Incorporating custom Python libraries into MLflow models for Databricks deployment
If your projects necessitate the integration of bespoke Python libraries or packages hosted on a secure private repository, MLflow provides a useful utility function, add_libraries_to_model. This feature allows you to seamlessly incorporate these custom dependencies into your models during the logging process, before deploying them via Databricks Model Serving. While the subsequent code examples demonstrate this functionality using scikit-learn models, the same methodology can be applied to any model type supported by MLflow:
- Upload dependencies and install them in the notebook: The recommended location for uploading dependency files is Databricks File System (DBFS):
dbutils.fs.cp("local_path/to/your_dependency.whl", "dbfs:/path/to/your_dependency.whl")# Installing custom library using %pip%pip install /dbfs/path/to/your_dependency.whl - Model logging with custom libraries...
