Fundamentals of Data Versioning You Must Know

Fundamentally, every possible way of changing datasets and the way we process our datasets (which naturally involves changing our code) represents an "experiment", and we want to keep track of every "experiment" we do. We need to manage versions of the data that were used to TRAIN, VALIDATE, and TEST ML models along with the ML models themselves. Data versioning means recording a specific moment across the evolution of data through a specific version number. This process in machine learning is valuable because the necessity of rolling back to a specific situation that brought us to the creation of a specific model cannot be overstated. Briefly and practically, to be able to reconstruct our project to a specific point in time, we must maintain the record of three objects: The code, the data, and the model.