Usually the first solution that comes to mind when trying to improve a machine learning model is to just add more training data. Additional data usually helps (barring certain situations) but generating high-quality data can be quite expensive. Hyperparameter optimization can save us time and resources by getting the best model performance using the existing data. Hyperparameter optimization, as the name suggests, is the process of identifying the best combination of hyperparameters for a machine learning model to satisfy an optimization function (i.e. In other words, each model comes with multiple knobs and levers that we can change, until we get to the optimized combination.

Businesses interact with their customers to better understand them and also to improve their products and services. This interaction can take the form of emails, textual social media posts (e.g. It would be inefficient and cost-prohibitive to have human representatives look through all of these forms of textual communications and then route the communications to the relevant teams to review, take action on and/or respond to customers. One inexpensive method to group such interactions and to assign them to relevant teams is using topic modeling. Topic modeling in the context of Natural Language Processing (NLP) is a type of unsupervised (i.e.