Welcome to kedro-mlflow’s documentation!¶
- Introduction
- Hello world example
- Getting Started
- Scope of the section
- Set up a Kedro project
- Configure mlflow
- Version parameters
- Version datasets
- What is artifact tracking?
- How to version data in a kedro project?
- Frequently asked questions
- Can I pass extra parameters to the
MlflowArtifactDataSet
for finer control? - Can I use the
MlflowArtifactDataSet
in interactive mode? - How do I upload an artifact to a non local destination (e.g. an S3 or blog storage)?
- Can I log an artifact in a specific run?
- Can I create a remote folder/subfolders architecture to organize the artifacts ?
- Can I pass extra parameters to the
- Version models
- Version metrics
- Opening the User Interface
- Package and serve a pipeline
- Python objects