It's the practice of applying DevOps principles to the machine learning lifecycle, which allows for automated training, deployment, and monitoring of models in the cloud.
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- How MLOps Bridges the Gap Between Data Science and Production
- Building Continuous Delivery Pipelines for Machine Learning at Scale
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