Cloud-Native Enabling Medical AI Innovations For UK-Based Healthcare Startup
Industry: Healthcare Provider
Clinical validation was reduced from ten hours to less than twenty minutes.
Computing requirements for AI experiments: 1600 CPU, 3.2 TB RAM
Instant access to compute, which used to take hours or days before.
Client Overview:
The client is a value-based care company and a digital-first health service provider that blends an artificial intelligence-powered platform with virtual clinical operations for patients. Through its web and mobile applications, patients are connected with health care experts.
In 2019, there wasn't enough processing capacity in-house to execute a particular experiment because many of the client's goods rely on machine learning and artificial intelligence. In addition, the company was expanding (from 100 to 1500+ employees in three years) and preparing to expand into new nations. All of these requirements along with the company's mission to provide accessible and affordable healthcare to everyone necessitated a creative answer. The client sought outside help to accomplish these objectives.
AiRo Builds A Self-Service AI Training Platform With Kubeflow
In 2018, the client moved its user-facing applications to the Kubernetes platform. AiRo's Cloud Professionals recognized this and turned to Kubeflow, a Kubernetes toolkit for machine learning. As a result, the entire project was built on top of a Kubernetes core server and deployed through Kubeflow.
The team used the Cloud Native Landscape to build out the stack after deciding to build the client's AI Research platform on top of Kubernetes: Prometheus and Grafana for monitoring; Istio service mesh to control the network on the training platform and control what access all of the workflows would have; Helm to deploy the stack, and Flux to manage the GitOps part of the pipeline.
AiRo’s Cloud-Native Approach Wins Client Confidence And Yields Lucrative Results
After implementing AiRo's solution, the client saw that teams now had rapid access to information and data instead of having to wait hours or days to calculate. For example, clinical validations that used to take 10 hours might now be completed in less than 20 minutes. Furthermore, the cloud-native platform's mobility has allowed the client to expand into new nations.
The following are some of the project's highlights that the customer noticed:
Clinical Validation was reduced from ten hours to less than twenty minutes.
Computing requirements for ai experiments: 1600 CPU, 3.2 TB RAM
Instant access to compute, which used to take hours or days before.
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The AiRo Perspective
After implementing AiRo's solution, the client saw that teams now had rapid access to information and data instead of having to wait hours or days to calculate. For example, clinical validations that used to take 10 hours might now be completed in less than 20 minutes. Furthermore, the cloud-native platform's mobility has allowed the client to expand into new nations.
