Industry analysts including Gartner and Forrester have long noted that many organisations are failing to capitalise on their investment in analytics. Generally speaking, this results from a focus on model development and data science, however this then results in a struggle to integrate the models into business operations — the action that actually unlocks the value from analytics.

ModelOps is a framework or practice that has emerged to address this challenge and is inspired by the success of DevOps. Its focus is operationalising analytics, i.e. taking models from development to production, and therefore transforming modelling from an academic exercise to an economic benefit. Effectively, it activates the value of analytics by applying data science to decision-making within the organisation.

I have often heard ModelOps described as ‘sophisticated model management’. However, it is much broader than model management because it is supported by a wide range of technology, from data to decisions. It is also known as MLOps, DeepOps or AIOps and simply put is a framework that helps organisations take models from development to production effectively.

To better understand the complexities involved and what ModelOps looks like in practice, I’m going to cover the aspects that should be addressed in a series of articles. These articles cover the benefits, organizational framework, the supporting technologies and the sophistication levels of ModelOps. The content described draws on experiences helping organisations of many sizes, across many industries, assessing and implementing their ModelOps frameworks. As well as conversations with peers and research from within the industry.

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