7 Machine Learning
In this chapter:
- Training a machine learning model
- Using Azure Machine Learning
- DevOps for machine learning
- Orchestrating machine learning pipelines
This chapter focuses on the final major workload of a data platform: machine learning. Machine learning is becoming increasingly important as more and more scenarios are supported by artificial intelligence. We will talk about running machine learning in production, reliably and at scale. Figure 7.1 highlights our current focus area.
Figure 7.1 Running machine learning at scale is the other major workload any data platform needs to support, besides data processing and analytics.
We’ll start with a machine learning model that a data scientist might develop on their laptop. This is a model that predicts whether a user is going to be a high spender or not, based on their web telemetry. The model is very simple, as the main focus is not its implementation, rather how we can take it and run it in the cloud.
The next section introduces Azure Machine Learning (AML), an Azure service for running ML workloads. We’ll spin up an instance, configure it, then take our model and run it in this environment. We’ll talk about the benefits of using Azure Machine Learning for training models.