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Why Monitoring Machine Learning Models Matters
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Monitoring machine learning models is crucial for any business that has operating ML models in production. This can be even more important than some other software systems that DevOps teams are more used to monitoring for multiple reasons. Firstly, ML models may fail silently if not monitored, and thus errors can potentially go undetected for a long time. Additionally, ML models are often some of the more essential components in the larger software systems. They are responsible for making intelligent decisions, and we rely on their predictions heavily.Despite the importance of monitoring machine learning models in production, there is not yet a standard practice or framework for doing this, and thus many models go into production without proper monitoring and testing. This is because the technology of ML models is only starting to mature, and MLOps, the intersection of DevOps and ML, is still a new field. In this post we will discuss the importance of machine learning model monitoring and potential issues that may arise with your model in the production environment. Illustration of the full lifecycle of a machine learning model from conception to production. In practice, ...Read More on Datafloq
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