Automation ML pipelines with OpenShift AI

Learning path | 2 resources | 1 hr | Published on June 7, 2024
Ramakrishna Yekulla Ian Lawson Alex Krikos

Dive into the end-to-end process of building and managing machine learning (ML) pipelines using OpenShift AI.

Try it in our no-cost Developer Sandbox

  1. Overview: Automation ML pipelines with OpenShift AI
  2. Set up and execute the pipeline Page | 1 hr

Overview: Automation ML pipelines with OpenShift AI

automation-pipeline-lp-feature-image

In the domain of machine learning (ML), the proficient management of trained models is essential. Using the Pipelines features provided in Red Hat OpenShift AI offers a robust solution to automate and streamline the ML lifecycle. This learning path delves into crafting a customized component for elevating the efficiency of model creation and management within the pipeline options provided by OpenShift AI.

Prerequisites:

  • Access to the Developer Sandbox (or your own OpenShift cluster).
  • A kaggle account (you need a kaggle.json access token file - go to https://www.kaggle.com and register/logon, then create/download the API token from the account settings)

In this learning path, you will:

  • Craft a customized component for OpenShift AI.
  • Automate pipeline runs.