ArangoML Series: Multi-Model Collaboration

00ArangoML, General, Graphs, Machine LearningTags: , , ,

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.
ArangoML Pipeline Complete pipeline - ArangoDB Machine Learning
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ArangoML Series: Intro to NetworkX Adapter

00ArangoML, General, Graphs, how to, Machine LearningTags: , , , , ,

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 here to better fit the blog post format and provide a quick introduction to using the NetworkX adapter. 

ArangoML Pipeline Cloud graphic showing an example machine learning pipeline
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ArangoML Part 4: Detecting Covariate Shift in Datasets

00ArangoML, General, Graphs, Machine LearningTags: ,

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
ArangoML Pipeline Complete pipeline - ArangoDB Machine Learning
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Do Graph Databases Scale? Yes? No? Let’s see!

00cluster, General, Graphs, Performance

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it. 

All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks.

But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see…

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