Utilizing Databricks Workflows with Jobs to automate model training and testing

In this section, we’ll delve into the powerful synergy between Databricks Workflows and Jobs to automate the training and testing of machine learning models. Before we jump into hands-on examples, it’s essential to understand the significance of automation in the ML life cycle and how Databricks uniquely addresses this challenge.

Automating the training and testing phases in machine learning is not just a convenience but a necessity for scalable and efficient ML operations. Manual processes are not only time-consuming but also prone to errors, making automation a critical aspect of modern MLOps.

This is where Databricks Workflows comes in and allows for the orchestration of complex ML pipelines.

Let’s take a look into an example workflow that we will automate using Workflows with Jobs. We will be going through the following logical steps shown in Figure 8.2:

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