MLOps (a compound of “machine learning” and “operationalization”) is the practice of operationalizing and managing the lifecycle of ML in production.
MLOps establishes a culture and environment where ML technologies can generate business benefits by optimizing the ML lifecycle to automate and scale ML initiatives and optimized business return of ML in production.
MLOps enables collaboration across diverse users (such as Data Scientists, Data Engineers, Business Analysts and ITOps) on ML operations and enables a data driven continuous optimization of ML operations’ impact or ROI (Return on Investment) to business applications.
This site is sponsored by ParallelM, whose mission is to promote awareness of ML Operationalization. MLOps.org encourages community engagement and invites content from all sources.