So far in this book, we have looked at three different machine learning algorithms to solve the predictive maintenance problem with the NASA Turbofan run to failure dataset. We recorded the results to MLflow. We can see that our XGBoost notebook outperformed the more complex neural networks. The following screenshot shows the MLflow result set showing the parameters and their associated scores.
From here we can download our model and put it in our web service. To do this we are going to use a Python Flask web service and Docker to make the service portable. Before we start, pip install the python Flask package. Also install Docker onto your local computer. Docker is a tool that allows you to build out complex deployments.
