Graph-powered ML-as-a-service accelerates insight to innovation.
ArangoDB, the company behind the most complete graph data and analytics platform, released two new products to make machine learning more accessible: ArangoGraphML (closed beta) and Jupyter Notebooks-as-a-service (open beta).
ArangoGraphML provides enterprise-ready, graph-powered machine learning (ML) available as a cloud service helping both experts and non-experts turn deeper insights into more powerful innovations. Jupyter Notebooks-as-a-service provide fast and secure data exploration for busy data scientists by keeping graph data in the cloud. Both of releases are additional tools in ArangoDB’s already extensive ML toolset, which includes a healthy ecosystem of plug-and-play adapters for cuGraph, DGL, NetworkX, and PyG.
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“Traditional machine learning misses connections and relationships between data points, which is where graph shines,” said Jörg Schad, Ph.D., ArangoDB CTO. “Graph machine learning is one of the most exciting trends in machine learning, but currently only accessible to large enterprises with dedicated teams of data scientists. ArangoDB wants to empower everyone to leverage graph technologies, including machine learning, to create business value. ArangoGraphML is another exciting step in this direction.”
ArangoGraphML makes it easy to develop reusable machine learning models without needing specialized data science training, as well as support data scientists with model development, management, and deployment. Initial features include:
Node classification as a service
Node classification is at the heart of many machine learning tasks. For example, is a node representing a seller account in an online marketplace classified as fraudulent (selling counterfeit goods) or not? ArangoGraphML makes it easier for data scientists to perform this core part of their job via an intuitive user interface, or an API call from other machine learning tools that they use.
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MLOps
To help data science teams focus on high-value tasks, ArangoGraphML includes MLOps to simplify the process of creating machine learning pipelines. Features include model training and management, hyperparameter optimization, metadata, lineage tracking of models and other artifacts, metrics, and dataset management.
GPU support
To uncover machine learning insights faster, ArangoGraphML runs on GPUs (graphics processing units). GPUs are silicon chips that can run computation tasks in parallel and therefore much faster than traditional CPUs, providing increased performance when analyzing large, distributed graphs.
Packaging and availability
Both ArangoGraphML and managed Jupyter Notebooks are available via ArangoGraph Insights Platform, a fully-managed, next-generation graph data and analytics platform. To get started with ArangoGraphML, request access to the beta. To use Jupyter Notebooks-as-a-service, create an ArangoGraph Insights Platform account.
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