IN PRACTICE
A US-based Fortune 100 health insurer was losing over 4,000 hours of productivity per month and facing budget overruns amounting to millions of dollars. Why? Because it was attempting to scale its operations to serve over 500 AI services, but without machine learning operations (ML Ops) or an effective governance structure. This was cumbersome for IT production and operational management and wasted time and money. Time for transformation The insurer discovered that Fractal’s combination of AI, engineering and design expertise could help it define the ML Ops and governance strategy it needed to operate its AI services. As a result, it set Fractal an ambitious goal: to establish the architecture and run a pilot. With massive potential savings at stake, time was of the essence. Rising to the challenge The Fractal team started by reviewing the insurer’s existing technology and use cases that need to be served. It identified a prioritization framework and defined a roadmap to implement a scalable ML Ops framework in the business and could immediately identify the cost-saving potential. It then aligned all stakeholders, including IT operations, and specified the optimal technologies considering existing governance and IT guidelines. The chosen technologies included an in-house automated machine learning (AutoML), a customized feature store to manage over 17,000+ features for batch and real-time services, and MLflow to orchestrate CI/CD (continuous integration and continuous
deployment) as well as model registry and model versioning. These technology components’ scalability, serviceability, and maintainability uniquely matched the client’s requirements in relation to feasibility, desirability, and viability. Once this had been organized, the Fractal team set up a specialist engineering unit to selectively scale high-priority AI services in an ML Ops framework. It then deployed and tested the pilot service end-to-end and ramped up the ML Ops services to incorporate other scalable technologies. Rapid results A design-driven architecture and strategy enabled the Fractal team to deliver the engagement rapidly. With a further investment of 24 weeks, the pilot engagement on ML Ops was complete, and the client organization realized significant time and cost savings. The ongoing scaling of further ML models was reduced from months to weeks. The future is now bright. The Fractal implementation has opened a path to fast and cost-effective expansion of the insurer’s AI services, enabling it to meet the needs of over 40 million customers in real-time and at scale. With massive potential savings at stake, time was of the essence.
How 40 million customers were
IN BRIEF
A Fortune 100 health insurer was losing 4,000 hours per month, and billions of dollars.
It needed an ML Ops platform and effective governance structure for 500+ AI models.
Fractal planned and defined a new architecture and strategy in just 12 weeks.
Huge cost savings were gained as soon as the pilot was complete.
The insurer now has a platform for expanded AI services to over 40 million customers in real-time.
given access to AI in healthcare insurance in record time.
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