lakeFS VS MLflow

# Download the LakeFS binary wget https://github.com/treeverse/lakeFS/releases/latest/download/lakefs # Make the binary executable chmod +x lakefs # Initialize LakeFS with S3 as the storage backend ./lakefs init --backend s3 --s3-gateway-endpoint --s3-region --s3-force-path-style --s3-access-key --s3-secret-key

Might want to look at purpose built tools for that such as lakeFS (https://github.com/treeverse/lakeFS/)

* Disclaimer: I'm one of the creators/maintainers of the project.

Might want to checkout lakeFS: https://github.com/treeverse/lakeFS

(full disclosure: I'm one of the creators)

Take a look at https://github.com/treeverse/lakeFS -

I mean if you're ready to adopt a new framework into your ecosystem this is one of the major usecases for LakeFS.

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It includes features for experiment tracking, model versioning, and deployment, enabling developers to track and compare experiments, package models into reproducible runs, and manage model deployment across multiple environments.

Platforms such as MLflow monitor the development stages of machine learning models. In parallel, Data Version Control (DVC) brings version control system-like functions to the realm of data sets and models.

MLFlow - http://mlflow.org

MLflow:

In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.

I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.

Hi, you should have a look at ML flow https://mlflow.org or weight and biases https://wandb.ai/site