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Nebius package for H2O-3

Description

H2O is an open-source, in-memory, distributed platform for machine learning and predictive analytics on big data. Built with speed and scalability in mind, H2O enables you to effortlessly build and deploy high-quality models in an enterprise environment. H2O’s core code, written in Java, leverages a distributed key/value store and a distributed MapReduce framework for seamless data access and processing across nodes. With intelligent data parsing and support for multiple formats, H2O simplifies data ingestion from various sources.

Short description

A distributed, in-memory machine learning and predictive analytics platform.

Use cases

  • Financial services: credit scoring, personalized offers, customer churn, anti-money laundering, trading strategies.

  • Healthcare: clinical workflow, capacity simulation, diagnosis assistance, infectious disease forecasting, pricing, claims assessment.

  • Insurance: fraud detection and mitigation, claims management, personalized rate management and product bundling.

  • Manufacturing: predictive design and maintenance, transportation optimization.

  • Marketing: audience segmentation, content personalization, next best offer and next best action.

  • Retail: assortment and price optimization.

  • Telecom: customer support, fleet maintenance.

Links

Legal

By using the application, you agree to their terms and conditions: the helm-chart, Apache 2.0.

Tutorial

{% note warning %}

If you want to use H2O-3 with GPUs and have them in your node group, install the NVIDIA® GPU Operator on the cluster.

{% endnote %}

Before installing this product:

  1. Create a Kubernetes cluster and a node group in it.
  2. Install kubectl, and configure it to work with the created cluster.

To install the product:

  1. Click Install.
  2. Wait for the application to change its status to Deployed.

Usage

  1. Install kubectl and configure it to work with the created cluster

  2. To check that H2O-3 is working, launch H2O Flow, the H2O-3 user interface:

    1. Configure port forwarding:

      kubectl port-forward <application-name>-0 8080:54321 -n <namespace>

    2. Go to http://localhost:8080 in your web browser.