MLOps

MLOps, or Machine Learning Operations, refers to a set of practices aimed at streamlining the deployment, monitoring, and maintenance of machine learning models in production. It integrates machine learning (ML), development (Dev), and operations (Ops) to enhance collaboration and efficiency throughout the model lifecycle. MLOps seeks to automate and standardize workflows for building, testing, and deploying ML models, thereby improving their reliability and scalability. By incorporating practices such as continuous integration and continuous delivery (CI/CD), version control, and automated testing, MLOps helps teams manage the complexities of ML systems. This approach not only accelerates time-to-market but also ensures models remain accurate and functional over time.