MLOps is a practice for collaboration and communication between data scientists and
operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle – from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics. 
In this podcast, Red Hat will host expert data scientist and CEO of Technology Partner cnvrg.io, Yochay Ettun, to discuss core practices in MLOps that will help data science teams scale on OpenShift. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline.