Originally built in the 1970s

MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. It bridges the gap between machine learning development and production operations, ensuring that models are robust, scalable, and aligned with business goals. The word is a compound of \"machine learning\" and the continuous delivery practice (CI/CD) of DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems. When an algorithm is ready to be launched, MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems. Similar to DevOps or DataOps approaches, MLOps seeks to increase automation and improve the quality of production models, while also focusing on business and regulatory requirements. While MLOps started as a set of best practices, it is slowly evolving into an independent approach to ML lifecycle management. MLOps applies to the entire lifecycle - from integrating with model generation, orchestration, and deployment, to health, diagnostics, governance, and business metrics.

Shiva Star, originally just SHIVA, is a high-powered pulsed-power research device located at the Air Force Research Laboratory on the Kirtland Air Force Base in Albuquerque, New Mexico. The device was originally built in the 1970s for high-power x-ray research, was later re-directed to studies for the Strategic Defense Initiative (SDI), and is now being used for magnetized target fusion research. Shiva Star was named after the Hindu god Shiva, partly because its prototype originally had four \"arms\"; it later got six \"arms\".