ModelOps Explained

Learn how ModelOps helps organizations deploy, run, and scale ML models

ModelOps Explained

ModelOps (also known as MLOps, or Machine Learning Operations) is the practice of incorporating machine learning models into an organization's software development and infrastructure operations. It aims to improve the efficiency, agility, and reliability of the machine learning development process, while also ensuring that models are deployed and managed in a way that is aligned with the organization's business objectives.

To achieve these goals, ModelOps involves the integration of machine learning workflows into the software development lifecycle (SDLC), as well as the deployment and management of machine learning models in production environments. This may involve processes such as automated model training and evaluation, continuous integration and delivery (CI/CD) of machine learning models, and the monitoring and maintenance of deployed models.

ModelOps also involves the development of infrastructure and tools to support the machine learning development process. This may include the use of platforms and frameworks for building, training, and deploying machine learning models, as well as tools for managing and monitoring deployed models.

Overall, the goal of ModelOps is to enable organizations to rapidly and effectively develop, deploy, and manage machine learning models in production environments, while also ensuring that these models are aligned with business objectives and deliver value to the organization.

ModelOps Video Explainer

In this video, Modzy Head of Product and co-founder, Seth Clark, discusses all things ModelOps, and considerations for building out the right pipeline for your team.