The article discusses the credibility of using Random Forest Variable Importance for identifying causal links in data where the output is binary. It contrasts this method with fitting a Logistic Regression model and examining its coefficients. The discussion highlights the challenges of extracting causality from observational data without controlled experiments, emphasizing the importance of domain knowledge and the use of partial dependence plots for interpreting model results.
In this article, we explore how to deploy and manage machine learning models using Google Kubernetes Engine (GKE), Google AI Platform, and TensorFlow Serving. We will cover the steps to create a machine learning model and deploy it on a Kubernetes cluster for inference.
