Model Deployment & Employment: Best Practices for Accurate and Reliable Machine Learning
As the complexity of machine learning models varies, the boundary between training and employment may become blurred. However, maintaining a clear separation is essential for effective model employment. By treating models as assembled tools and following recommended practices, such as separating scripts, avoiding the use of employment methods for training, and reporting results separately, accurate and reliable model employment can be achieved.
After completing the training phase of machine learning models, the subsequent stages of the modeling pipeline are activated, namely model deployment and model employment.
- Model deployment refers to placing the trained model in a specific location.
- Model employment encompasses the practical use of the model, including making predictions, interpreting features, and determining significant parameters.
While model deployment is the physical placement of the model, model employment is about using the model’s capabilities for various purposes.
By maintaining a clear separation between model training, deployment, and employment, the machine learning community has achieved remarkable advancements in the field.
In contrast to the machine learning community, the classical statistics community traditionally conflated model training and employment. That is because, in classical statistics, models were trained to test hypotheses and identify significant differences in features related to a particular response variable. Both the training and employment phases were performed using the same dataset, leading to a lack of differentiation between the two stages. This approach did not emphasize the importance of separating training and employment.