Summary

In this chapter, you learned about the origins of data science and how it relates to machine learning. You then learned about the iterative nature of a data science project and discovered the various phases you will be working on. Starting from the problem understanding phase, you will then acquire and explore data, create new features, train a model, and then deploy to verify your hypothesis. Then, you saw how you can scale out the processing of big data files using the Spark ecosystem. In the last section, you discovered the DevOps mindset that helps agile teams be more efficient, meaning that they develop and deploy new product features in short periods of time. You saw the components that are commonly used within an MLOps-driven team, and you saw that in the epicenter of that diagram, you find AzureML.

In the next chapter, you will learn how to deploy an AzureML workspace and understand the Azure resources that you will be using in your data science journey throughout this book.