Speaker:
Amit Kesarwani, Director, Solution Engineering, LakeFS
Abstract:
Machine learning workflows are not linear, where experimentation is an iterative & repetitive to and fro process between different components. What this often involves is experimentation with different data labeling techniques, data cleaning, preprocessing and feature selection methods during model training, just to arrive at an accurate model.
Quality ML at scale is only possible when we can reproduce a specific iteration of the ML experiment-and this is where data is key. This means capturing the version of training data, ML code and model artifacts at each iteration is mandatory. However, to efficiently version ML ex...