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Who’s Who in Data Science

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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Community Notebook Challenge

Estimated reading time: 2 minutes

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 ..

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ArangoBnB: Data Preparation Case Study

Estimated reading time: 20 minutes

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..

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ArangoML Series: Multi-Model Collaboration

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.
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ArangoML Series: Intro to NetworkX Adapter

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

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
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ArangoML Part 3: Bootstrapping and Bias Variance

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
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ArangoML Part 2: Basic Arangopipe Workflow

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
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ArangoML Part 1: Where Graphs and Machine Learning Meet

Estimated reading time: 5 minutes

Estimated reading time: 4 minutes

This post is the first in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. In this first post, we look at what exactly ArangoML is, with later posts in this series showcasing the different tools and use cases.

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Best Practices for AQL Graph Queries

Estimated reading time: 9 minutes

Estimated reading time: 8 minutes

The ArangoDB Query Language(AQL) was designed to accomplish a few important goals, including:

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