Calling all Community Members! 🥑

Today we are excited to announce our Community Notebook Challenge.

What is our Notebook Challenge you ask? Well, this blog post is going to catch you up to speed and get you excited to participate and have the chance to win the grand prize: a pair of custom Apple Airpod Pros.

Our Interactive Tutorials repository has a library of python notebooks available covering the full spectrum of ArangoDB features. Although we cover a lot of topics, something is missing… YOUR notebook! As announced in our July Newsletter on July 29th and running until October 31st, the ..

Estimated reading time: 8 minutes

My name is Laura, and I am responsible for digital marketing here at ArangoDB. 

In the following post, I will dive into my own experience working at ArangoDB and how I ended up from Northern Ontario, Canada to work in Germany at a native multi-model graph database company. Are you interested in learning more about working abroad, working remotely, or diving into a new industry? This post covers all of the above topics.

Estimated reading time: 12 minute

This post will dive into the world of Natural Language Processing by using word embeddings to search movie descriptions in ArangoDB.

In this post we:

• Discuss the background of word embeddings
• Introduce the current state-of-the-art models for embedding text
• Apply a model to produce embeddings of movie descriptions in an IMDb dataset
• Perform similarity search in ArangoDB using these embeddings
• Show you how to query the movie description embeddings in ArangoDB with custom search terms

Estimated reading time: 18 minutes

This case study covers a data exploration and analysis scenario about modeling data when migrating to ArangoDB. The topics covered in this case study include:

• Importing data into ArangoDB
• Developing Application Requirements before modeling
• Data Analysis and Exploration with AQL

This case study can hopefully be used as a guide as it shows step-by-step instructions and discusses the motivations in exploring and transforming data in preparation for a real-world application.The information contained in this case study is derived from the development of the..

Estimated reading time: 19 minutes

Over the past years, we have seen a shift in processors from the previously dominant x86 architecture to the more energy-efficient (and often cheaper) ARM architecture. This trend is true for both consumer hardware, e.g., Apple’s M1 SoC and also server hardware, e.g. AWS’s Gravitron processor. Given that the ARM architecture has a more relaxed memory model, this might have some subtle impact on C++ programs…

With the introduction of multi-threaded execution in C++11, the standard also introduced a memory model. For many developers, this memory model is..

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.

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..

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

• 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

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