MLOps

Operational Efficiency in Machine Learning

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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.

product Benefits

Need for Operationalizing ML

Time & Cost

Excessive time from ML model development to production causes delays in business value realization.

Management

Lack of continuous integration (CI), continuous deployment (CD), quality assurance (QA), and monitoring environment makes the overall ML deployment broken, hard to manage, error-prone, time-consuming, and expensive.

Compliance

The current practice of ML deployment makes it hard to produce explainable models and generate reports for any regulatory compliances.

Accure 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

Benefits

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

  • Model Metadata

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.

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