Open Data Hub 0.4.0 Release Guide

Author: lavlas
Publication Date: September 16, 2019
Category: features,roadmap,release

What is included?

Open Data Hub 0.4.0 includes many new tools that are essential to a comprehensive AI/ML end-to-end platform. Open Data Hub is a meta-operator that can be installed on Openshift Container Platform 3.11 and 4.

The following is a list of tools added to the Open Data Hub in this release:

Technology Version Category
Open Data Hub Operator 0.4.0 Meta Operator Application management
Argo 2.3.0 Container native workflow engine
Strimzi Kafka Operator 0.11.1 Distributed streaming platform
Open Data Hub AI-Library 1.0 Machine learning as a service

You can review the release notes for components added in the v0.3.0 release here

Argo

Argo is an open source container-native workflow engine for orchestrating parallel jobs on Kubernetes. It is useful for defining workflows using containers, running computer intensive jobs, and running CI/CD pipelines natively on Kubernetes.

To learn more about deploying Argo in the Open Data Hub, please visit link

Strimzi Kafka Operator

Strimzi provides a way to run an Apache Kafka cluster on Kubernetes in various deployment configurations. Apache Kafka is a distributed streaming platform for publishing and subscribing records as well as storing and processing streams of records.

Strimzi is based on Apache Kafka 2.0.1 and consists of three main components:

  • Cluster Operator Responsible for deploying and managing Apache Kafka clusters within OpenShift or Kubernetes cluster.

  • Topic Operator Responsible for managing Kafka topics within a Kafka cluster running within OpenShift or Kubernetes cluster.

  • User Operator Responsible for managing Kafka users within a Kafka cluster running within OpenShift or Kubernetes cluster.

To learn more about using the Strimzi operator to deploy an Apache Kafka cluster in the Open Data Hub, please visit link

Open Data Hub AI-Library

AI-Library is an open source collection of AI components that allows for rapid prototyping of ideas. AI-Library enables users to work with machine learning models without worrying about infrastructure issues, model complexity or any data science expertise.

To learn more about deploying AI-Library models in the Open Data Hub, please visit link