Summary
In this chapter, you learned how the AzureML Python SDK is structured. You also discovered the AzureML notebook editor, which allows you to code Python scripts. You then worked with the SDK. You started your coding journey by managing the compute targets that are attached to the AzureML workspace. You then attached new datastores and got a reference to existing ones, including the default datastore for the workspace. Then, you worked with various files and tabular-based datasets and learned how to reuse them by registering them in the workspace.
Finally, you worked with the AzureML CLI extension, which is a client that utilizes the Python SDK you explored in this chapter.
In the next chapter, you will build on top of this knowledge and learn how to use the AzureML SDK during the data science experimentation phase. You will also learn how to track metrics on your data science experiments, as well as how to scale your training into bigger computes, by running scripts in...
