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Machine Learning Operations (MLOps) are essential to build successful Data Science use-cases. Today, ML is powering data driven use-cases that are transforming industries around the world. In order to seize and hold it's competitive advantage business needs to reduce risk therefore a new expertise rises to include data science models in operational systems. According to Gartner Research “While many organizations have experimented with AI proofs of concept, there are still major blockers to operationalizing its development. IT leaders must strive to move beyond the POC to ensure that more projects get to production and that they do so at scale to deliver business value. (July 2020)”. In this session, we will discuss the role of MLOps and how they can help data science models from deployment to maintenance with focus on: keep track of performance degradation overtime from model predictions quality, setting up continuous evaluation metrics and tuning the model performance in both training and serving pipelines that are deployed in production.