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Designing ML Systems - Data distribution shifts and monitoring in production

Machine learning models have a lifespan. They decay over time and can make mistakes or predictions that don't make sense. They can also encounter unexpected or corrupted data that affect the quality of the outputs. When you deploy a machine learning model in production, it faces real-world data. As the environment changes, this data might differ from what the model has seen during training. As a result, the so-called data drift can make the model less accurate. To address this, you need ML model monitoring. This video explores the topic of Data distribution shifts and monitoring in production in detail.