What is MLOps?
MLOps is a set of practices that aims to streamline the process of deploying and maintaining ML models in production environments. It involves integrating ML workflows with DevOps practices to ensure that ML models are tested, deployed, and monitored in a reliable and scalable manner.
MLOps involves collaboration between data scientists, ML engineers, software developers, and operations teams to create a continuous integration and delivery pipeline for ML models. This pipeline includes steps such as data preparation, model training, model testing, model deployment, and monitoring. MLOps also involves the use of automation, testing, and monitoring tools to ensure that models remain accurate, stable, and scalable over time.
Some common practices in MLOps include version control for models and data, experiment tracking, model validation, continuous integration and delivery, containerization of models, and monitoring of model performance and data drift.
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