They generate an overwhelming amount of data that you have to ingest, transfer, prepare and store even before thinking of analyzing them or training a model. In this demo, we will explain how we architected and deployed a variety of data engineering patterns to create a full edge-to-core data & machine learning pipeline on OpenShift/Kubernetes for a smart-city use case.
We will walk you through the approach we took to deploy the ML model on K8s, moving data from edge-to-core using kafka, creation of data aggregation pipelines, and demo of real-time and batch analysis etc. Our overarching goal was to possess the ability to re-deploy the entire stack with a single command, for which we used Ansible and we lived happily thereafter. By the end of this session, you should get a better understanding on how to architect and develop data engineering workflows, and how to automate the deployment of the entire stack using Ansible.
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