AI/ML Lifecycle Automation at the Edge

Lab overview

This project showcases how the integration of AI and edge computing can effectively address specific customer use cases and fit seamlessly into various real-world scenarios. The demo will highlight the practical application of AI in resource-constrained environments, making it essential to use lightweight topologies and platforms such as Single Node OpenShift (SNO) and Red Hat Device Edge.

During the demo, we will develop a robust solution for the automotive industry, highlighting the importance of automation, as dedicated teams are not feasible at the edge. To address this challenge, our solution incorporates key components within Red Hat OpenShift AI to support the entire AI/ML lifecycle at the edge, including model training, data science pipelines, model serving, and model monitoring.

Lab contents

To bring this demo to life, several technologies will be used and covered during this lab. Below is a summary of the materials that will be covered:

Chapter Duration Contents

AI & Edge Use case

10min

  • Understanding our case

  • Essentials of Edge

  • Explore our design

Dashboards Control Center

1min

  • Control center access

Autonomous Vehicle

15min

  • Understand the vehicle infrastructure

  • Deplying MinIO storage and models

  • Deplying Inference servers

  • Deploying the Battery Monitoring System

RHOAI configuration

15min

  • RHOAI installation

  • Datascience projects

  • DataConnections

Model Re-training

15min

  • Workbenches

  • Import Notebooks

  • Training our models

Model Serving

10min

  • Stress Detection serving

  • Time to Failure serving

  • Querying endpoints

Pipelines Automation

5min

  • Pipeline server

  • Pipelines execution

  • Pipelines scheduled automation

Model Monitoring

5min

  • TrustyAI

  • Bias detection

  • Data drift

Battery Monitoring System

15min

  • Test alerting system