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Hey r/MachineLearning. I'm one of the creators of DAGsHub, and I wanted to share something cool that we've been working on. DVC (dvc.org) and MLflow (mlflow.org) are two open-source projects that are very widely adopted, each for its own specialty. DVC excels at data versioning, and MLflow is used for many things (it's actually multiple tools combined into one), but mainly for its experiment tracking capabilities. Both tools have a built-in tradeoff – since they are open source, setting up storage for DVC and a central tracking server for MLflow can be a pain – requiring you to create cloud accounts, add permissions, and more.
Hey r/MachineLearning. I'm one of the creators of DAGsHub, and I wanted to share something cool that we've been working on. DVC (dvc.org) and MLflow (mlflow.org) are two open-source projects that are very widely adopted, each for its own specialty. DVC excels at data versioning, and MLflow is used for many things (it's actually multiple tools combined into one), but mainly for its experiment tracking capabilities. Both tools have a built-in tradeoff – since they are open source, setting up storage for DVC and a central tracking server for MLflow can be a pain – requiring you to create cloud accounts, add permissions, and more.