MLOps refers to a collection of practices that facilitate collaboration and communication between data scientists and operations professionals. By implementing these practices, organizations can enhance the quality, streamline the management process, and automate the deployment of Machine Learning and Deep Learning models in large-scale production environments. MLOps enables better alignment of models with business requirements and ensures compliance with regulatory standards.
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View Code? Open in Web Editor NEWA comprehensive MLOps structure that includes the use of CI/CD pipelines, DVC, MLflow, Git workflow, and Heroku. This structure covers the complete lifecycle of machine learning operations, from continuous integration and deployment pipelines to version control with DVC, experiment tracking with MLflow, collaborative development with Git workflow,