Data Change Management
Continuous changes in data can severely impact the quality of machine learning models and dashboards, leading to unreliable predictions and performance issues. Unlike traditional DevOps, tracing problems in ML models is complex due to their black-box nature, making it challenging to identify the root cause of failures. Implementing robust change management processes is essential to ensure reliability and minimize disruptions.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
787: MLOps: The Job and The Key Tools — with Demetrios Brinkmann
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