Reviewing the Azure Machine Learning life cycle

No matter what technology or framework we choose to work with to develop our ML project, there are four phases we go through. Each stage has one or more steps, depending on the individual scenario. The ML life cycle is significant because it clearly outlines every project step. Then, it is easy to break the project into tasks and assign them to the person responsible because, usually, more than one role is involved in an ML project.

Let us review all the stages before we connect them to the components of Azure Machine Learning.

ML life cycle

In ML, we identify four stages: business understanding, data operations, model training, and model deployment. As shown in the following figure, these stages are part of an iterative process:

Figure 1.2 – ML life cycle

Let us go through each step of this iterative process and what it entails, starting with the business understanding stage and the gathering...