Designing your training system
Viewed at the highest level, ML models go through a life cycle with two stages: a training phase and an output phase. During the training phase, the model is fed data to learn from the dataset. In the prediction phase, the model, complete with its optimized parameters, is fed new data in order and returns the desired output.
These two phases have very different computational and processing requirements. In the training phase, we have to expose the model to as much data as we can to gain the best performance, all while ensuring subsets of data are kept aside for testing and validation. Model training is fundamentally an optimization problem, which requires several incremental steps to get to a solution. Therefore, this is computationally demanding, and in cases where the data is relatively large (or compute resources are relatively low), it can take a long time. Even if you had a small dataset and a lot of computational resources, training is still...
