Validated Patterns

Intel AMX accelerated Medical Diagnosis

Validation status:
Sandbox Sandbox

About the Medical Diagnosis pattern


This validated pattern is a modified version of the Medical Diagnosis pattern. It was extended to showcase 4th Generation Intel Xeon Scalable Processors capabilities, especially Intel AMX that speeds up AI workloads. The pattern is based on a demo implementation of an automated data pipeline for chest X-ray analysis that was previously developed by Red Hat. It includes the same functionality as the original demonstration, but uses the GitOps framework to deploy the pattern including Operators, creation of namespaces, and cluster configuration.

Compared to the original Medical Diagnosis pattern, this one was extended by the Node Feature Discovery Operator, whose task is to detect hardware features and expose them as labels for this node. Red Hat OpenShift Serverless component is modified to assign AI workload to nodes with available Intel AMX feature.

Moreover, the Machine Learning model was quantized to int8 precision to improve efficiency thanks to Intel AMX, with only marginal accuracy loss.

This pattern was also adapted to run in on-premise environments.

  • Node Feature Discovery operator labels nodes with Intel AMX capabilities.

  • Ingest chest X-rays from a simulated X-ray machine and puts them into an objectStore based on Ceph.

  • The objectStore sends a notification to a Kafka topic.

  • A KNative Eventing listener to the topic triggers a KNative Serving function.

  • KNative Serving was modified to schedule pods with AI workload only on nodes with enabled Intel AMX feature

  • An ML-trained model running in a container makes a risk assessment of Pneumonia for incoming images.

  • A Grafana dashboard displays the pipeline in real time, along with images incoming, processed, anonymized, and full metrics collected from Prometheus.

The simplified pipeline without Intel AMX is showcased in this video.


About the solution elements

The solution aids the understanding of the following:

  • How to use a GitOps approach to keep in control of configuration and operations.

  • How to deploy AI/ML technologies for medical diagnosis using GitOps.

The Medical Diagnosis pattern uses the following products and technologies:

  • Red Hat OpenShift Container Platform for container orchestration

  • Red Hat OpenShift GitOps, a GitOps continuous delivery (CD) solution

  • Red Hat AMQ, an event streaming platform based on the Apache Kafka

  • Red Hat OpenShift Serverless for event-driven applications

  • Red Hat OpenShift Data Foundation for cloud native storage capabilities

  • Grafana Operator to manage and share Grafana dashboards, data sources, and so on

  • Node Feature Discovery Operator to label nodes with Intel AMX capabilities

About the architecture

Presently, the Intel AMX accelerated Medical Diagnosis pattern does not have an edge component. Edge deployment capabilities are planned as part of the pattern architecture for a future release.

edge medical diagnosis marketing slide

Components are running on OpenShift either at the data center, at the medical facility, or public cloud running OpenShift.

The diagram below shows the components that are deployed with the the data flows and API calls between them.

physical dataflow

Next steps