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    How ArangGraphML Leverages Intel’s PyG Optimizations

    December 03, 2023

    Tags: ArangoGraphML, Alliances, Intel Xeon CPU, Partners, Machine Learning, Intel, Intel PyG

    Estimated reading time: 4 minutes

    Estimated reading time: 3 minutes

    ArangoGraphML + Intel: Next-level Machine Learning Accelerated

    ArangoDB and Intel have announced a groundbreaking partnership to enhance Graph Machine Learning (GraphML) using Intel’s high-performance processors. This collaboration, part of the Intel Disruptor Program, will seek to integrate ArangoDB’s graph database solutions with Intel’s Xeon CPU. This synergy promises to revolutionize data analytics and pattern recognition in complex graph structures, marking a new era in database technology and GraphML advancements.

    (more…)

    Introducing the ArangoDB-PyG Adapter

    August 04, 2022

    Tags: ArangoML, Machine Learning, how to, Graphs, General, Graph Neural Networks, PyG, pytorch, PyTorch Geometric, torch

    Estimated reading time: 1 minutes

    Estimated reading time: 10 minutes

    We are proud to announce the GA 1.0 release of the ArangoDB-PyG Adapter!

    The ArangoDB-PyG Adapter exports Graphs from ArangoDB, the multi-model database for graph & beyond, into PyTorch Geometric (PyG), a PyTorch-based Graph Neural Network library, and vice-versa.

    On July 29 2022, we introduced the first release of the PyTorch Geometric Adapter to the ArangoML community. We are proud to have PyG as the fourth member of our ArangoDB Adapter Family. You can expect the same developer-friendly adapter options and a helpful getting-started guide via Jupyter..

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    Who’s Who in Data Science

    April 14, 2022

    Tags: Data Science, Future-of-nosql, collaboration, analytics, AQL, ArangoML, Publication, Machine Learning, General, networkx, ML

    Estimated reading time: 10 minutes

    Estimated reading time: 10 minutes

    Multiple data science personas participate in the daily operations of data logistics and intelligent business applications. Management and employees need to understand the big picture of data science to maximize collaboration efforts for these operations. This article will highlight the specialized roles and skillsets needed for the different data science tasks and the best tools to empower data-driven teams. You will come away from this article with a better understanding of how to support your own data science teams, and it is valuable for both managers..

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    Integrate ArangoDB with PyTorch Geometric to Build Recommendation Systems

    April 06, 2022

    Tags: ArangoML, Machine Learning, Graph, how to, Graphs, General, recommendation, pytorch

    Estimated reading time: 1 minutes

    Estimated reading time: 20 minutes

    In this blog post, we will build a complete movie recommendation application using ArangoDB and PyTorch Geometric. We will tackle the challenge of building a movie recommendation application by transforming it into the task of link prediction. Our goal is to predict missing links between a user and the movies they have not watched yet.

    Run this notebook yourself: https://colab.research.google.com/github/arangodb/interactive_tutorials/blob/master/notebooks/Integrate_ArangoDB_with_PyG.ipynb

    (more…)

    A Comprehensive Case-Study of GraphSage using PyTorchGeometric and Open-Graph-Benchmark

    August 13, 2021

    Tags: ArangoML, Machine Learning, Graph, how to, Graphs, General, graphsage, python, pytorch, ML

    Estimated reading time: 1 minutes

    Estimated reading time: 15 minute

    This blog post provides a comprehensive study on the theoretical and practical understanding of GraphSage, this notebook will cover:

    • What is GraphSage
    • Neighbourhood Sampling
    • Getting Hands-on Experience with GraphSage and PyTorch Geometric Library
    • Open-Graph-Benchmark’s Amazon Product Recommendation Dataset
    • Creating and Saving a model
    • Generating Graph Embeddings Visualizations and Observations
    (more…)

    ArangoML Series: Multi-Model Collaboration

    January 26, 2021

    Tags: ArangoML, Machine Learning, Graph, Graphs, General, python

    Estimated reading time: 9 minutes

    Estimated reading time: 8 minutes

    Multi-Model Machine Learning

    This article looks at how a team collaborating on a real-world machine learning project benefits from using a multi-model database for capturing ML meta-data.

    The specific points discussed in this article are how:

    • The graph data model is superior to relational for ML meta-data storage.
    • Storing ML experiment objects is natural with multi-model.
    • ArangoML promotes collaboration due to the flexibility of multi-model.
    • ArangoML provides ops logging and performance analysis.
    (more…)

    ArangoML Series: Intro to NetworkX Adapter

    November 17, 2020

    Tags: ArangoML, arangopipe, Machine Learning, Graph, how to, Graphs, General, networkx, python

    Estimated reading time: 4 minutes

    Estimated reading time: 3 minutes

    This post is the fifth in a series of posts introducing the ArangoML features and tools. This post introduces the NetworkX adapter, which makes it easy to analyze your graphs stored in ArangoDB with NetworkX.

    In this post we:

    • Briefly introduce NetworkX
    • Explore the IMDB user rating dataset
    • Showcase the ArangoDB integration of NetworkX
    • Explore the centrality measures of the data using NetworkX
    • Store the experiment with arangopipe

    This notebook is just a slice of the full-sized notebook available in the ArangoDB NetworkX adapter repository. It is summarized..

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    ArangoML Part 4: Detecting Covariate Shift in Datasets

    November 05, 2020

    Tags: ArangoML, Machine Learning, Graphs, General

    Estimated reading time: 2 minutes

    Estimated reading time: 1 minute

    This post is the fourth in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. Until now, we have focused on ArangoML’s ability to capture metadata for your machine learning projects, but it does much more. 

    In this post we:

    • Introduce the concept of covariate shift in datasets
    • Showcase the built-in dataset shift detection API
    (more…)

    ArangoML Part 3: Bootstrapping and Bias Variance

    October 24, 2020

    Tags: ArangoML, Machine Learning, Graphs, General

    Estimated reading time: 3 minutes

    Estimated reading time: 2 minutes

    This post is the third in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we:

    • Introduce bootstrapping and bias-variance concepts
    • Estimate and analyze the variance of the model from part 2
    • Capture the metadata for this activity with arangopipe
    (more…)

    ArangoML Part 2: Basic Arangopipe Workflow

    October 09, 2020

    Tags: ArangoML, Machine Learning, Graphs, General

    Estimated reading time: 2 minutes

    Estimated reading time: 1 minute

    This post is the second in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we:

    • Introduce machine learning concepts
    • Demonstrate basic model building
    • Log a model building activity with arangopipe
    (more…)
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