Fog Computing Developer in Toowoomba, Queensland, Australia

Anonymous users visit websites every day, yet companies are forced to present non-personalized content to them. I developed a machine learning approach to track and identify anonymous users against demographics identifying the same user (with a high degree of confidence) on multiple devices and from any of the attribution channels.

The system was self-validating using the feedback of converted users to validate and correct the machine learning models daily.

Microservice dependency, configuration, and secret management were nonexistent at the time; a few isolated approaches existed but nothing with permanence.

Partnering with three universities and 35 industry publications, I created and open-sourced a universal microservice language using machine learning.

These are maximum security minimal footprint production base image/language containers.

Most secure, CI/CD compatible and automatically updated optimized base container available. Used in several enterprises, and publishes a daily vulnerability CVE list

This is a machine registration endpoint with no prior traffic should automatically register new machine users while being able to resist port scanning and DDoS attacks while maintaining cost by not triggering auto-scale events for false traffic.