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Why a Top 5 Global Insurer Chose Verta for MLOps

For insurance companies, success rests almost entirely on making well-informed predictions about future risks. That’s why actuaries were arguably the original data scientists. Over time, insurers have grown to use machine learning not just for underwriting and loss prediction but also to process claims efficiently, reduce customer churn, and improve digital marketing and personalization. 

Results at a Glance


  • $4 million cost savings due to not hiring additional IT and engineering FTEs for operations
  • Estimated team size reduced from 50 FTEs to 12 FTEs
  • 2+ years accelerated time-to-market, delivering faster innovation and competitive advantage
  • Improved MLOps UI/UX with canary deployment and autoscaling to reduce 24/7 support needs

This insurer has many different data science teams associated with different branches of the business, and managing machine learning operations was a huge burden. As the number of models grew, the Platform team helping to manually scale model deployment was stretched thin, and hiring new engineers required a significant lead time of 3–6 months. 

The insurer was eager to get models to production faster and centralize their model deployments with a comprehensive MLOps platform.

As with many large companies, they initially looked at building a solution in-house. However, ultimately, they found that this approach would be more expensive and time-consuming than a vendor solution like Verta.

What the Customer Says

The customer was thrilled with the end result, saying:

We are much better off already having gone with the Verta platform than we would have been, because people have already left the organization that were going to be working on our DIY build....Verta covered operational aspects better than anyone. All other vendors were very shallow on real operations capabilities. Frankly, their capabilities were indications they don’t understand operations.”

In total, the insurer estimates that they have saved $4 million with Verta and accelerated their time to market with an MLOps platform by 2+ years.

Rather than needing to hire 40 additional FTEs, they can maintain a small team by deploying models from Verta’s central platform and having a single person aligned with each model deployment.

You can read more about this partnership and the results Verta generated by reading our case study.

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