MLOps (Machine Learning Operations) refers to a set of practices for data scientists, ML engineers, and ML operations professionals to help facilitate the deployment, monitoring and maintenance of their ML models in production, in a faster, more automated, and effective way.
Applying these practices, machine learning and deep learning models can be deployed in large-scale production environments more easily. It also serves to simplify model management, improve quality, automate the deployment process, and help ensure that models are best supporting business needs, regulations, and compliance.
MLOps is slowly emerging into an independent way of managing machine learning lifecycles. There are many aspects to this – data collection, model building (software development lifecycle, continuous integration/continuous delivery), orchestration, deployment, health, diagnostics, governance, and business metrics.