Machine Learning (ML) model metrics are designed to monitor performance. But when a model goes into production, many factors influence its performance. The traditional checkpoints may no longer help as organisations look to scale these models (think: scaling from a million to billion credit card users). This is why experts advocate for MLOps, a branch of ML that brings together all the nice things from DevOps and ML.
Though a few experts hold MLOps as the best solution available right now, it’s still beset by ambiguities. To address these, Deeplearning.ai recently hosted a panel of MLOps experts to derive insights on the most important aspects of production machine learning and what MLOps looks like at companies. Hosted by Ryan Keenan of Deeplearning.ai, the panel consisted of A
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Ram Sagar
I have a master's degree in Robotics and I write about machine learning advancements.
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