As artificial intelligence (AI) and machine learning (ML) technologies continue to proliferate, using ML models plays a crucial role in converting the insights from data into actual business impacts. Operational ML means streamlining every step of the ML lifecycle and deploying the best models within the existing production system. And within that production system, the models may interact with various processes, such as testing, performance tuning of IT resources, and monitoring strategy and operations.
One common pitfall is a lack of model performance monitoring and proper model retraining and updating, which could adversely affect business. Nearly continuous model monitoring can provide information on how the model is performing in production. The monitoring outputs are used to identify the problems proactively and take corrective actions, such as model retraining and updating, to help stabilize the model in production. However, in a real-world production setting, multiple personas may interact with the model,

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