Thanks to the rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) across industries, AI and ML have found a place in the common vocabulary. Almost every sector of the industry (healthcare, e-commerce, IoT, banking & finance, etc.) are leveraging AI and ML to streamline business operations and create innovative products/services. So, when everyone in the industry is using AI and ML, what can you do differently to up your game? The answer is MLOps or Machine Learning Operationalization. In simple terms, MLOps is the Machine learning equivalent of DevOps.

Thanks to the rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) across industries, AI and ML have found a place in the common vocabulary. Almost every sector of the industry (healthcare, e-commerce, IoT, banking & finance, etc.) are leveraging AI and ML to streamline business operations and create innovative products/services. So, when everyone in the industry is using AI and ML, what can you do differently to up your game? The answer is MLOps or Machine Learning Operationalization. In simple terms, MLOps is the Machine learning equivalent of DevOps.

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.

Gartner's 2019 CIO Survey found that the number of enterprises implementing AI grew 270 percent in the past four years. That's all impressive, well and good – but how many of those projects have evolved past the POC stage, how many have gone into production and been scaled across the enterprise? AI implementations are up, but Gartner also reported last October that by 2021, more than half of machine learning projects will not be fully deployed because of operational problems. That's the key challenge in enterprise AI today: getting past the project phase, what IBM calls "chapter 2" for AI. Last November, HPE acquired BlueData, maker of container-based software for AI deployment and management.