What are the different approaches to data versioning in machine learning?

Anton Knight
Anton KnightAnswered

In machine learning, data versioning is the practice in machine learning of keeping many iterations of the data that will be used to test and refine a model. This guarantees repeatability, monitors evolution, and facilitates teamwork. In machine learning, data versioning may be broken down into two: manual and automated.

Manual vs Automated data versioning

Manually: It is recommended to create backups of the data at various points during the project, including before and after data cleansing and feature engineering. It’s possible to keep the information in a variety of places and file types, either on a local disk or on the cloud. This can be time-consuming and error-prone, and it may become challenging to monitor the effects of any modifications.

Automatically: Data versions may be created and managed automatically with the use of software designed for automated data versioning. Software revision management solutions and DVC machine learning tools like Git, Mercurial, and Subversion (SVN) may help with this. These programs facilitate teamwork, record modifications, and let you revert to a prior version if required. Cloud systems like Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure provide data versioning tools that may be included in an ML pipeline.

Data versioning tools like Databricks and MLflow have native support for data versioning. You can keep track of changes to your data, code, and models in one central location and even revert to older versions of your data if necessary. This is an especially helpful feature when numerous groups collaborate on the same project or when dealing with a huge dataset versioning.

What is data versioning?

Data versioning in machine learning refers to keeping different iterations of the data needed to train and test a machine learning model.

  • In machine learning, data versioning may be broken down into two: manual and automated.

Creating and managing several data versions by hand is known as Manual Data Versioning. Using dedicated software, data versioning is producing and maintaining multiple copies of a dataset without human intervention. It is a feature offered by certain machine learning platforms. Dataset size, project complexity, and available resources are all factors to consider while deciding on a strategy.

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