Minitab Model Ops

Minitab Model Ops efficiently close the divide between model creation and model deployment using a user-friendly, yet robust MLOps platform. With the increasing prevalence of machine learning applications, the demand for deploying and operationalizing models has become crucial. It provides a comprehensive solution that empowers business analysts and engineers to effectively implement their own machine learning and predictive models. Users can effortlessly upload, deploy, and delete models using the Model Library functionality.

Minitab Model Ops helps to deploy-

With just a single click, effortlessly deploy your models to gain instant insights. Importing models and storing them in your personalized model repository is a breeze. Each model in it has the capability to publish a REST API endpoint, ensuring seamless integration into modern enterprise applications.

Minitab Model Ops helps to monitor & manager-

Ensure the performance of your models from any location with ease. Monitor crucial drift and stability metrics for each model and establish critical thresholds. Access real-time information on model uptime, response time, and deployment status. Receive immediate alerts as changes occur, keeping you informed and proactive in managing your models.

Minitab Model Ops helps to govern-

Safely deliver your models while ensuring compliance through a comprehensive audit log. Maintain control over access, track changes, and establish a traceable update history, guaranteeing regulatory compliance and mitigating audit risks.

Features of Minitab Model Ops

It offers various useful features. The Model Library feature allows users to easily upload, deploy, and remove models. With Model Monitoring, users can access useful reports such as drift reports and stability reports to monitor the performance of their models. Additionally, the system provides email alerts to notify users of any significant changes or issues detected. To ensure transparency and accountability, the system also maintains comprehensive audit logs for both the system and the models used.