AI/ML Data Pipeline Processing with Go Microservices based solution at the Edge using Open-Source Technology
AI/ML Open-Source solutions are becoming more prevalent with the increased availability of data along with faster and low-cost processing compute. Neethu, alongside her team, created an AI/ML Data pipeline Processing infrastructure to enable healthcare use cases with open source EdgeX Foundries microservices that automatically detect, manage, and process images received from OEM equipment. This project enabled managing AI/ML pipelines for image processing and automating image comparisons, and developing an adaptable solution for different use cases and settings, leveraging a containerized microservices based architecture and various communication APIs & messaging bus including MQTT. This project included interesting challenges such as a distributed deployment at the edge scenario where some services ran on windows-based OEM equipment, while others ran on a Linux Edge box, timing-dependent issues leading to a lack of idempotency in integration tests, and discussions around the idea of adding wait strategy for dependent services to be up and ready before accepting TCP connections. This session will cover the architecture & design, with integration of EdgeX features to develop this solution for Edge devices along with the learnings from this open-source project.
Open-Source code for setup, install, and execution of software, with complete developer documentation - https://github.com/intel/AiCSD
Date and Time
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- Date: 19 Oct 2023
- Time: 12:00 PM to 01:00 PM
- All times are (UTC-07:00) Arizona
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neethu.elizabeth.simon@intel.com
- Co-sponsored by Neethu Elizabeth Simon
- Starts 05 October 2023 09:00 AM
- Ends 19 October 2023 01:00 PM
- All times are (UTC-07:00) Arizona
- No Admission Charge
Speakers
Neethu Elizabeth Simon
AI/ML Data Pipeline Processing with Go Microservices based solution at the Edge using Open-Source Technology
AI/ML Open-Source solutions are becoming more prevalent with the increased availability of data along with faster and low-cost processing compute. Neethu, alongside her team, created an AI/ML Data pipeline Processing infrastructure to enable healthcare use cases with open source EdgeX Foundries microservices that automatically detect, manage, and process images received from OEM equipment. This project enabled managing AI/ML pipelines for image processing and automating image comparisons, and developing an adaptable solution for different use cases and settings, leveraging a containerized microservices based architecture and various communication APIs & messaging bus including MQTT. This project included interesting challenges such as a distributed deployment at the edge scenario where some services ran on windows-based OEM equipment, while others ran on a Linux Edge box, timing-dependent issues leading to a lack of idempotency in integration tests, and discussions around the idea of adding wait strategy for dependent services to be up and ready before accepting TCP connections. This session will cover the architecture & design, with integration of EdgeX features to develop this solution for Edge devices along with the learnings from this open-source project.
Open-Source code for setup, install, and execution of software, with complete developer documentation - https://github.com/intel/AiCSD
Biography:
Neethu Elizabeth Simon is an IOT/ML Senior Software Engineer in the Network & Edge Group at Intel Corporation, with vast industrial experience in building containerized microservices for computer vision-based AI/ML solutions for different domains including retail, industrial, healthcare etc. Recipient of 2020 Society of Women Engineers Distinguished New Engineer Award, Neethu shares her technical proficiency through conference speakership, leading & reviewing technical conferences, patent filings, book publications and STEM volunteering. Neethu holds a Master’s in Computer Science from Arizona State University and is passionate about promoting girls in STEM, women in technology, Diversity and Inclusion.
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Media
| Open-Source AI Pipeline Processing Microservices | Talk on Intel AI/ML Microservice for image processing in Health care by IEEE WIE member Neethu Elizabeth Simon. The solution can be used to efficiently deploy any supported OpenVINO pipeline on Intel CPU . | 3.25 MiB |