QA for Machine Learning Models With the PDCA Cycle

The primary goal of establishing and implementing Quality Assurance (QA) practices for machine learning/data science projects or projects using machine learning models is to achieve consistent and sustained improvements in business processes, making use of underlying ML predictions. This is where the idea of the PDCA cycle (Plan-Do-Check-Act) is applied to establish a repeatable process ensuring that high-quality machine learning (ML)-based solutions are served to the clients in a consistent and sustained manner. The following diagram represents the details:

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