I'll add the value of machine learning is the dynamic nature of the solution. In a production situation most likely, retraining quickly with weaker hyperparameters every day would lead to a higher total applied accuracy than retraining once a month with hyperparam tuning. IF the hyperparam solution is actually better, then the problem space is very static, and you might want to rethink your ML approach