Download PDF
Empowering Life Insurers with Epigenetics and AI
Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Predictive Maintenance
- Machine Condition Monitoring
Services
- Data Science Services
- Cloud Planning, Design & Implementation Services
The Challenge
FOXO Technologies is a biotechnology company that aims to make longevity accessible to all using epigenetic science. They use machine learning to examine thousands of models to find patterns of DNA methylation that classify human health, wellness, disease, and aging. Their mission is to help people live longer, healthier lives. However, the data science team at FOXO found it challenging to scale as they looked to build thousands of predictive models based on 860,000 DNA probes. They needed a solution that could help them build, fine-tune, deploy, and manage models in production at scale.
About The Customer
FOXO Technologies is a biotechnology company that focuses on making longevity accessible to all using epigenetic science. They use advanced machine learning to find patterns that classify human health, wellness, disease, and aging. Their mission is to help people live longer, healthier lives. They collect quantitative data for over 860,000 DNA methylation probes corresponding to different sites along the human genome. Their findings assist with underwriting decisions and mortality prediction, revolutionizing the insurance industry and eliminating the need for invasive blood testing.
The Solution
FOXO turned to DataRobot’s AI Cloud Platform to address their challenge. The platform allowed FOXO data scientists to build, fine-tune, deploy, and manage models in production all in one place. This significantly increased the team's efficiency. Along with efficiency gains, FOXO values DataRobot’s high-level security and privacy, which are critical factors for the company and its life insurance partners. DataRobot automates the process, shortcutting the time to build and manage each model. This allows their team members to focus on the more strategic aspects of modeling, giving them greater job satisfaction and helping attract and retain data science talent.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
IoT enabled Fleet Management with MindSphere
In view of growing competition, Gämmerler had a strong need to remain competitive via process optimization, reliability and gentle handling of printed products, even at highest press speeds. In addition, a digitalization initiative also included developing a key differentiation via data-driven services offers.
Case Study
Remote Monitoring & Predictive Maintenance App for a Solar Energy System
The maintenance & tracking of various modules was an overhead for the customer due to the huge labor costs involved. Being an advanced solar solutions provider, they wanted to ensure early detection of issues and provide the best-in-class customer experience. Hence they wanted to automate the whole process.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Siemens Wind Power
Wind provides clean, renewable energy. The core concept is simple: wind turbines spin blades to generate power. However, today's systems are anything but simple. Modern wind turbines have blades that sweep a 120 meter circle, cost more than 1 million dollars and generate multiple megawatts of power. Each turbine may include up to 1,000 sensors and actuators – integrating strain gages, bearing monitors and power conditioning technology. The turbine can control blade speed and power generation by altering the blade pitch and power extraction. Controlling the turbine is a sophisticated job requiring many cooperating processors closing high-speed loops and implementing intelligent monitoring and optimization algorithms. But the real challenge is integrating these turbines so that they work together. A wind farm may include hundreds of turbines. They are often installed in difficult-to-access locations at sea. The farm must implement a fundamentally and truly distributed control system. Like all power systems, the goal of the farm is to match generation to load. A farm with hundreds of turbines must optimize that load by balancing the loading and generation across a wide geography. Wind, of course, is dynamic. Almost every picture of a wind farm shows a calm sea and a setting sun. But things get challenging when a storm goes through the wind farm. In a storm, the control system must decide how to take energy out of gusts to generate constant power. It must intelligently balance load across many turbines. And a critical consideration is the loading and potential damage to a half-billion-dollar installed asset. This is no environment for a slow or undependable control system. Reliability and performance are crucial.
Case Study
Integration of PLC with IoT for Bosch Rexroth
The application arises from the need to monitor and anticipate the problems of one or more machines managed by a PLC. These problems, often resulting from the accumulation over time of small discrepancies, require, when they occur, ex post technical operations maintenance.
Case Study
Refinery Saves Over $700,000 with Smart Wireless
One of the largest petroleum refineries in the world is equipped to refine various types of crude oil and manufacture various grades of fuel from motor gasoline to Aviation Turbine Fuel. Due to wear and tear, eight hydrogen valves in each refinery were leaking, and each cost $1800 per ton of hydrogen vented. The plant also had leakage on nearly 30 flare control hydrocarbon valves. The refinery wanted a continuous, online monitoring system that could catch leaks early, minimize hydrogen and hydrocarbon production losses, and improve safety for maintenance.