Treat your data and models the same way you treat your code. Use tools that let you track changes and keep everything organized.
Store raw datasets in clearly named folders or cloud buckets so you always know which version you’re using. Tools like Data Version Control (DVC) help track every step in your pipeline, while MLflow or Weights & Biases can log model weights and experiments.
Tag production-ready versions and keep audit trails so nothing gets lost or overwritten. This keeps your team aligned and makes debugging much easier down the road.
