MLOps, short for Machine Learning Operations, is the practice of incorporating DevOps practices and principles into machine learning workflows. It applies software engineering practices such as version control, continuous integration, and continuous deployment to develop, deploy, and manage machine learning models. The MLOps process starts with data collection and preparation and continues through the model training, testing, and deployment. It also emphasizes monitoring, logging, and alerting to ensure the smooth operation of machine learning models in production environments. MLOps helps organizations scale machine learning operations, reduce risk, and increase collaboration and agility.
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