Extract live data collection from images and logs

Learning path | 2 resources | 1 hr | Published on May 20, 2025
Ramakrishna Yekulla Alex Krikos Ian Lawson

Explore the complete machine learning operations (MLOps) pipeline utilizing Red Hat OpenShift AI, storing models in object storage like S3, fetching models in OpenShift AI, and model serving.

Try it in the Developer Sandbox

  1. Overview: Extract live data collection from images and logs
  2. Complete the four phases of the MLOps pipeline Page | 1 hr

Overview: Extract live data collection from images and logs

Extract-live-data-lp-feature-image

The world of machine learning (ML) is rife with potential, but transitioning robust models from experimentation to production can be a complex journey. This is where machine learning operations (MLOps) come in, bridging the gap between data science and application development. Let’s explore how Red Hat OpenShift AI, a powerful MLOps platform, empowers you to streamline your ML workflow.

Prerequisites:

In this learning path, you will:

  • Store an ML model in object storage, such as AWS S3.
  • Deploy the model on OpenShift AI.
  • Use Python code within a Flash application to connect and send image prediction requests to the deployed model on OpenShift AI.