| | A Guide to Version Control for Machine Learning | | | | | | Hi David, It's a well known fact that Machine Learning (ML) requires a lot of trial and error. Experimentation is key. The procedures that people use to prepare training data and tune training parameters are very iterative. In order to facilitate this kind of software development you have to track the code, configurations, and data used for ML experiments so you can always answer the question of how a model was trained. However, large training datasets often preclude traditional version control software from being used for this purpose. In these cases, MapR Snapshots provides a highly attractive solution for data versioning. Join us on Tuesday March 5th when we will explain how MapR Snapshots work and demonstrate them with Valohai, which is software designed to manage ML experiments with robust version control for models, model configs, and training data. In this webinar you will learn: - How to perform data versioning in files, tables, and/or streams with MapR Snapshots.
- How to perform ML experiments where you want to vary training parameters but work from a known and unchanging version of training data.
- How to achieve ML audit compliance, so you’re better able to explain how production models were derived and which data versions you used to test against important things like gender and racial bias.
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