AI/ML open-source solutions are becoming more prevalent with the increased availability of data along with faster and low-cost processing computing. Our team developed an AI/ML Data pipeline processing infrastructure to enable healthcare use cases with open source (and Go-based) EdgeX Foundries microservices that automatically detect, manage, and process images received from OEM equipment. So basically, we enabled AI/ML pipelines for image processing and automating image comparisons - all while leveraging a Go containerized microservice-based architecture and various communication APIs and MQTT message bus. In developing this healthcare solution, we encountered several interesting challenges and Go packages to include: * A distributed deployment at the edge scenario with some Go microservices running on Windows-based OEM equipment and others running on a Linux edge box. * The need for services to wait for their dependent services to be up and ready before accepting TCP connections. * Backend status internationalization capabilities. * Classic idempotency issues in integration tests due to timing issues on various developers’ machines. This unique project introduced the ability to consume some interesting Go packages. There were challenges and learnings along the way, that I look forward to sharing with you!