Chapter 10: Building, Deploying, and Monitoring Your Model
In the previous chapter, you built the data pipeline and created a basic flight dataset that can be used by your data science team. In this chapter, your data science team will use the flight dataset to build a machine learning (ML) model. The model will be used to predict the on-time performance of the flights.
In this chapter, you will see how the platform assists you in visualizing and experimenting with the data to build the right model. You will see how to tune hyperparameters and compare the results of different runs of model training. You will see how to register and version models using the components provided by the platform. You will deploy the model as a REST service and start monitoring the deployed model using the components provided by the platform.
Remember that this book is not about data science, instead, the focus is on enabling teams to work autonomously and efficiently. You may see some concepts and...
