DVC vs MLflow vs W&B: Which Saves More Debug Hours?

The Experiment That Broke My Git History

Three weeks into a computer vision project, I had 47 model checkpoints scattered across five directories, a experiments_final_v3_REAL.csv file, and no idea which hyperparameters produced my best validation score. Sound familiar?

This isn't a theoretical comparison. I've used all three tools—DVC, MLflow, and Weights & Biases—on production projects, and each one solved different problems while creating new ones. The internet is full of feature matrices, but nobody talks about what actually breaks at 2 AM when you're trying to reproduce last month's results.

Photo by Telmo Antunes on Pexels

DVC: Git for Data (With Git's Learning Curve)

DVC (Data Version Control) takes a fundamentally different approach than the other two. It doesn't run a server. It doesn't have a fancy web UI. It just extends Git to handle large files and pipelines.

Here's what the workflow looks like:

python
# Initialize DVC in your repo
# dvc init
# dvc remote add -d myremote s3://my-bucket/dvc-storage

# Track a large dataset

---

*Continue reading the full article on [TildAlice](https://tildalice.io/dvc-mlflow-wandb-experiment-tracking-comparison/)*