Operational Efficiency in Machine Learning

Why MLOps?

On average, it takes 3 months to take an ML model from development to production. Data Scientists use different tools, programming languages, and libraries to develop models that are challenging for the operations teams to efficiently deploy, manage, monitor and scale. Frequent model enhancements and iterations make the situation even worse.

Need for Operationalizing ML

Momentum MLOPs

We’ve developed MLOps that streamlines ML operations and significantly reduces model deployment time, providing explainability and governance.

Product Features

  • Data scientists develop models in the language of their choice and are able to publish them to production right from their code.
  • Supports CI/CD via multiple protocols (Restful, cURL, web UI) 
  • Network and application security
  • Role-based access
  • Model governance
  • Model registry
  • Version control and A/B testing
  • Data drift detection
  • Model performance and drift monitoring
  • Monitoring dashboard and reporting
  • Lightweight and highly scalable


1-click deployment with CI/CD will reduce the time from 3 months to minutes and streamline ML operations

Before: 3 Months Deployment Cycle

Now: CI/CD With Real-Time Monitoring

Product Screenshots

Ready To Embrace The Future

If you are working on a data engineering or AI solution, trying to explore a use case, or building a proof-of-concept, please contact us for a one-on-one discussion.