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Novuna Enhances Model Deployment Efficiency with an AI-Powered MLOps Framework

novuna case study image-01

Industry

Financial Services

Challenge

Novuna were investing heavily in AI and ML, but their existing CI/CD processes weren’t designed for the complexities of ML. Manual steps, inconsistent deployments, and operational bottlenecks were slowing down innovation.

Results

We designed and implemented a production-ready MLOps framework, resulting in improved reliability, stronger governance and scalable foundations.

Solution type

MLOps, AI

www.novuna.co.uk
60%
Faster deployment times

"This collaboration has revolutionised our deployment process, enabling us to bring predictive models into production with unprecedented efficiency and reliability."

Kingsley James

Lead Data Scientist, Novuna

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Novuna, a leading financial services provider in the UK, was investing heavily in artificial intelligence and machine learning. But despite building high-performing models, they faced a familiar challenge: moving those models into production quickly, reliably, and at scale.

Their existing CI/CD processes weren’t designed for the complexities of ML. Manual steps, inconsistent deployments, and operational bottlenecks were slowing down innovation.

To overcome this, Novuna partnered with Advancing Analytics to design and implement a production-ready MLOps framework. The result? A faster, more reliable, and fully scalable machine learning deployment process.

The Challenge: Operationalising ML at Scale 

Like many enterprise organisations, Novuna found that developing models was only half the battle. The real challenge was operationalising ML models—getting them out of notebooks and into production environments.

Their current CI/CD pipeline lacked the automation, governance, and scalability needed for enterprise AI. Each deployment required heavy manual intervention, increasing risk and reducing efficiency. Without a standardised process, progress was slow, and innovation was held back.

The Solution: AI-Powered MLOps Framework 

Advancing Analytics worked closely with Novuna to deliver a tailored MLOps solution built for real-world financial services use cases. The team designed a custom MLOps framework to accelerate deployment while meeting the security and compliance requirements of a highly regulated industry.

Key technologies included:

  • Azure DevOps for end-to-end CI/CD automation
  • Azure Databricks for scalable model execution
  • JFrog Artifactory for secure version control and package management

This framework was implemented within Novuna’s dedicated Data Science Labs environment and built in collaboration with internal teams to ensure long-term adoption and scalability.

The Results: Faster, Safer AI Deployment 

The impact was immediate:

  • 60% faster deployment times, reducing time-to-value for new ML models
  • Improved reliability, with fewer failures and more consistent performance
  • Stronger governance, with version-controlled and auditable pipelines
  • Scalable foundations, enabling faster rollout of future AI use cases

With this MLOps framework in place, Novuna now has a repeatable, secure, and scalable process for delivering machine learning into production—turning innovation into impact faster than ever before.

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