In this chapter, we will learn how to use Azure in data science. We are going to look at how to prepare data, which includes cleaning and transforming it, creating engineering features, creating and training a machine learning model, and finally, making predictions using the machine learning model.
To build a machine learning algorithm with big data, we need to process a lot of data to train it. To do this, we need a lot of computing power. We also need the compute to be able to scale dynamically based on the load to serve these machine learning models at scale so that they can perform predictions.
In the era before public clouds, we had to buy all of our hardware beforehand. We had to pay for it all, whether or not we actually ended up using it. With Azure and other public clouds, we can now have different types of on-demand compute, based...
