ModelOps

ModelOps is a holistic approach for rapidly and iteratively moving models through the analytics life cycle so they are deployed faster and deliver expected business value. ModelOps is based on the application development community's DevOps approach. But where DevOps focuses on application development, ModelOps focuses on getting models from the lab through validation, testing and deployment phases as quickly as possible, while ensuring quality results. It also focuses on ongoing monitoring and retraining of models to ensure peak performance.

According to Gartner, "ModelOps (or AI model operationalization) is focused primarily on the governance and life cycle management of a wide range of operationalized artificial intelligence (AI) and decision models, including machine learning, knowledge graphs, rules, optimization, linguistic and agent-based models. Core capabilities include continuous integration/continuous delivery (CI/CD) integration, model development environments, champion-challenger testing, model versioning, model store and rollback."

“ModelOps lies at the center of any organizations’ enterprise AI strategy”. ModelOps is about creating a shared service that runs across the organization — enabling robust scaling, governance, integration, monitoring and management of various AI models. Adopting a ModelOps strategy should facilitate improvements to the performance, scalability and reliability of AI models. ModelOps aims to eliminate internal friction between teams by sharing accountability and responsibility. It protects the organization’s interests, both internally and externally. ModelOps (and its MLOps subset which focus on ML models only) is a key capability that is required for successful AI/ML model operations once models have been developed. It is a discipline that is separate and apart from model development. Industry experts and analysts are recognizing that model development and model operations are different disciplines, requiring different capabilities, tools and even teams. Gartner, in a recent article, states, “Platform independence: AI pipelines span multiple environments from developer notebooks to edge to data center to cloud deployments. A true ModelOps framework allows you to bring standardization and scalability across these disparate environments so that development, training and deployment processes can run consistently and in a platform-agnostic manner.”