Modelops 2022: the state of practice

How AI-focused corporate leaders are implementing best practices to ensure business success while governing and scaling their AI initiatives

Introduction to ModelOps

ModelOps help organizations to implement AI solutions, organize a path to operationalize AI at scale as well as integrate DataOps, ITOps and DevOps proficiency. As core enterprise capabilities, ModelOps includes the processes, the operations, the tools and the technologies that companies can use to deploy to monitor and even govern their machine learning models.

In ModelOps, predictive analytics and machine learning workflows are made operational, put to work and used to impact an organization, so that teams waste less time on tasks that should be done elsewhere and instead focus on the things that really matter to them. Modelops is undoubtedly an enterprise capability that allows everyone across the organization to be involved in the collaboration of AI and machine learning solutions with the aim to work together to maximize the potential of AI.

Your AI transformation is doomed without ModelOps — Forrester

As defined by Gartner, ModelOps is a set of practices that seek to automate a common set of operations that arise in data science projects, which include model training pipelines, version control, data management, experiment monitoring, testing and distribution. It aims to make all predictive analytics, machine learning, and AI models operational.

To put it simply, ModelOps involves taking predictive analytics and machine learning workflows and putting them to work, making them work actually, and using them to make an impact on day-to-day operations, rather than just providing insights. This is really about the difference between having static insight versus being a part of an organization that moves around and affects the way things happen.

Continue reading Modelops 2022: the state of practice