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There are many approaches and strategies for MLOPs in data science departments, each with their own focus, benefits, or pain points. From standardizing practices and way of work, to automating the boring tasks of your job, to extensive performance monitoring, to governance and control: MLOPs can take many forms, especially depending on which roles you talk to. We believe a successful MLOPs implementation includes everybody. In this demo we will walk through a crisis scenario and showcase how different roles approach drift detection, model iteration, and governance policies; all in the same platform, to ensure continuous business results and performance.
– Governance is a key aspect of MLOPs; we highlight some aspects of compliance you should watch out for / include in your strategy
– Model and data drift monitoring strategies
– Collaboration and different roles and responsibilities within the MLOPs process