Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
Data Version Control
dvc + DAGsHub sounds like a good lightweighted way for data version control.
dolthub is another good way for data version control.
Data Versioning, Data Pipelines, and Data Lineage
Retracing Your Steps in Machine Learning
Model Life Cycle Tracking
mlflow mlflow tracks every detail about a model (including training, servering, etc.) but it seems to be a little bit complicated to use.
References
Continuous Delivery for Machine Learning
https://towardsdatascience.com/version-control-ml-model-4adb2db5f87c
How to version control your production machine learning models