Deploy ArangoGraphML

You can deploy ArangoGraphML in your own Kubernetes cluster or use the managed cloud service that comes with a ready-to-go, pre-configured environment

Managed cloud service versus self-managed

ArangoDB offers two deployment options, tailored to suit diverse requirements and infrastructure preferences:

ArangoGraphML

ArangoGraphML provides enterprise-ready Graph Machine Learning as a Cloud Service via Jupyter Notebooks that run on the ArangoGraph Insights Platform .

To get access to ArangoGraphML services and packages, get in touch  with the ArangoDB team.
  • Accessible at all levels
    • Low code UI
    • Notebooks
    • APIs
  • Full usability
    • MLOps lifecycle
    • Metrics
    • Metadata capture
    • Model management

ArangoGraphML Pipeline

Setup

The ArangoGraphML managed-service runs on the ArangoGraph Insights Platform . It offers a pre-configured environment where everything, including necessary components and configurations, comes preloaded. You don’t need to set up or configure the infrastructure, and can immediately start using the GraphML functionalities.

To summarize, all you need to do is:

  1. Sign up for an ArangoGraph account .
  2. Create a new deployment in ArangoGraph.
  3. Start using the ArangoGraphML functionalities.

Self-managed ArangoGraphML

ArangoDB Enterprise Edition

The self-managed solution enables you to deploy and manage ArangoML within your Kubernetes cluster using the ArangoDB Kubernetes Operator .

The self-managed package includes the same features as in ArangoGraphML. The primary distinction lies in the environment setup: with the self-managed solution, you have direct control over configuring your environment.

Setup

You can run ArangoGraphML in your Kubernetes cluster provided you already have a running ArangoDeployment. If you don’t have one yet, consider checking the installation guide of the ArangoDB Kubernetes Operator  and the ArangoDeployment Custom Resource  description.

To start ArangoGraphML in your Kubernetes cluster, follow the instructions provided in the ArangoMLExtension Custom Resource  description. Once the CustomResource has been created and the ArangoGraphML extension is ready, you can start using it.

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