What is ModelOps?
As stated in Gartner, “ModelOps focuses on the governance and life cycle management of all AI and decision models that include knowledge graphs, rules, optimization, and linguistic and agent-based models. It includes core capabilities such management of model repository, model rollout/rollback, and CI/CD integration.”
In addition to these, it provides business domain experts autonomy to interpret the outcomes and validate KPIs of AI models in production. It also provides the ability to promote or demote the models for inferencing without dependency on data scientists or ML engineers
Why is ModelOps important?
ModelOps can help companies facing challenges with scaling their analytics and AI initiatives. It has the potential to help companies maximize and scale AI initiatives by providing the level of autonomy and transparency across business, development, and operations teams. It provides regular updates and deployments as AI models are being managed, monitored, retrained, and transitioned through various environments.
What are the benefits of ModelOps?
- It frees data scientists and model engineers to develop innovative solutions to improve business and customer experience.
- It automates time-taking model workflows and provides visibility into each step. Processes such as model review, testing, and approvals are automated to make the workflow faster and more efficient.
- It enables data scientists to continuously monitor and improve the model to ensure the data quality, accuracy, and performance required in enterprise models.
- It fosters collaboration between experts in different fields.
- It retains data lineage and tracks back information for governance and audit compliance.