Original article can be found here (source): Artificial Intelligence on Medium
Imagine that you’ve spent several months creating a machine learning (ML) model that can determine if a transaction is fraudulent or not with a near-perfect f1 score. That’s great, but you’re not done yet. Ideally, you would want your model to determine if a transaction is fraudulent in real-time so that you can prevent it from going through in time. This is where model deployment comes in.
Most online resources focus on the prior steps to the machine learning life cycle like exploratory data analysis (EDA), model selection, and model evaluation. However, model deployment is a topic that seems to be rarely discussed — the reason being is that it can be fairly complicated. Deployment is a topic that is completely unrelated from EDA, model selection, or model evaluation, and thus, it’s not well understood by those without a background in software engineering or DevOps. In this article, you’ll learn what model deployment is, the high-level architecture of a model, different methods in deploying a model, and factors to consider when determining your method of deployment.