Kubeflow

Learn how open source impacts watsonx through some of the key open source projects that IBM has invested in.

Challenges and benefits of the new Kubeflow Pipelines capabilities

In this learning path, learn how to use Kubeflow Pipelines to orchestrate ML workflows.

In this tutorial, you learn how to leverage Kubeflow Pipelines to improve and customize the large language models (LLMs) results by training the prompt tuning configuration based on your prompt datasets. This process can be automated and configured based on other new datasets and LLMs as well.

Learn how to leverage Kubeflow Pipelines to serve large language models (LLMs) with custom prompt tuning configuration. Also, learn how to extend Kubeflow Pipelines to cover the whole LLM development cycle from training the personalized prompt tuning output to serving custom LLM framework on KServe ModelMesh for any cutting-edge large language models.

In this tutorial, you learn how to set up a stand-alone Kubeflow Pipelines on a local Kubernetes cluster using **kind**. Then, you can use Kubeflow Pipelines as a local development environment to compose and run ML pipelines.

Use the Kubeflow Pipeline SDK to compose a simple pipeline and then run and view the results of a run on the Kubeflow Pipelines dashboard.

Learn how to deploy Kubeflow Pipelines along with Red Hat OpenShift Pipelines to get additional functions not readily available in Red Hat OpenShift Pipelines.

An open-source platform that allows users to create, deploy, and manage machine learning workflows on Kubernetes. Kubeflow Pipelines provides a way to define and execute complex pipelines consisting of interconnected steps, enabling users to easily orchestrate the entire machine learning process from data preparation to model training, evaluation, and deployment.

A machine learning toolkit that makes deployments of machine learning workflows on Kubernetes simple, portable, and scalable.