6. Reproducibility
Reproducibility means that every process within your ML systems should produce identical results given the same input. This has two main aspects.
The first one is that you should always know what the inputs are—for example, when training a model, you can use a plethora of hyperparameters. Thus, you need a way to always track what assets were used to generate the new assets, such as what dataset version and config were used to train the model.
The second aspect is based on the non-deterministic nature of ML processes. For example, when training a model from scratch, all the weights are initially randomly initialized. Thus, even if you use the same dataset and hyperparameters, you might end up with a model with a different performance. This aspect can be solved by always using a seed before generating random numbers, as in reality, we cannot digitally create randomness, only pseudo-random numbers. Thus, by providing a seed, we ensure that we always...
