Whether it’s handling and preparing datasets for model training, pruning model weights, tuning parameters, or any number of other approaches and techniques, optimizing machine learning models is a labor of love.
It’s important to note that there’s no one-size-fits-all approach: different use cases require different techniques, and various stages of the model building lifecycle determine possible and preferred optimization strategies.