Machine Learning Operationalization in the Enterprise

HPE ML Ops brings DevOps-like speed and agility to the entire machine learning lifecycle. As enterprises move beyond experimentation to more widespread adoption of AI, a vast majority of them are running into "last mile" issues related to model deployment and management. Gartner predicts that by 2021, at least 50 percent of machine learning models built with the intention of being operationalized will not see the light of day.1 What is "operationalization"? Admittedly, it's a mouthful--and some even abbreviate it as "o16n". But it's the biggest challenge facing enterprises as they embark on the next phase in their AI journey with machine learning (ML). Note: In this blog post, I'll refer primarily to ML, but the same applies to deep learning (DL), a subset of ML.