Keeping the data under version control with Git-LFS is a big improvement. But the lack of version control of the data files is not the entire problem.

The determining factors for the results of training a model or other activities include the following:

• Training data — the image database or whatever data source is used in training the model
• The scripts used in training the model
• The libraries used by the training scripts
• The scripts used in processing data
• The libraries or other tools used in processing data
• The operating system and CPU/GPU hardware
• Production system code
• Libraries used by production system code

Obviously the result of training a model depends on a variety of conditions. Since there are so many variables to this, it is hard to be precise, but the general problem is a lack of what’s now called Configuration Management.

DVC takes on and solves a larger slice of the machine learning reproducibility problem than does Git-LFS or several other potential solutions:

DVC workflow – code & data

Full article: Why Git and Git-LFS is not enough to solve the Machine Learning Reproducibility crisis