Phases of ML Workloads - Machine Learning Lens

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Phases of ML Workloads

Building and operating a typical ML workload is an iterative process, and consists of multiple phases. We identify these phases loosely based on the open standard process model for Cross Industry Standard Process Data Mining (CRISP-DM) as a general guideline. CRISP-DM is used as a baseline because it’s a proven tool in the industry and is application neutral, which makes it an easy-to-apply methodology that is applicable to a wide variety of ML pipelines and workloads.

The end-to-end machine learning process includes the following phases:

Figure 1 – End-to-End Machine Learning Process