ArangoDB ArangoML | Machine Learning with NoSQL

Who’s Who in Data Science

Christopher Woodward — Thu, 14 Apr 2022 15:04:10 +0000

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…

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Integrate ArangoDB with PyTorch Geometric to Build Recommendation Systems

Sachin Sharma — Wed, 06 Apr 2022 16:52:45 +0000

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…

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

Sachin Sharma — Fri, 13 Aug 2021 14:40:16 +0000

Estimated reading time: 15 minute This blog post provides a comprehensive study on the theoretical and practical understanding of GraphSage, this notebook will cover: Check it out on github Last updated: 23/08/2023 15:05:13 Hear More from the Author Graph Embeddings Continue Reading Entity Resolution in ArangoDB Inside the Avocado Grove: From Canada to Germany and the…

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ArangoML Series: Multi-Model Collaboration

Christopher Woodward — Tue, 26 Jan 2021 18:45:36 +0000

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: Be sure to check out the other posts in this series:ArangoML Part 1: Where Graphs and Machine Learning MeetArangoML…

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ArangoML Series: Intro to NetworkX Adapter

Christopher Woodward — Tue, 17 Nov 2020 18:25:00 +0000

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: This notebook is just a slice of the full-sized notebook available in the ArangoDB NetworkX adapter…

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ArangoML Part 4: Detecting Covariate Shift in Datasets

Christopher Woodward — Thu, 05 Nov 2020 09:31:00 +0000

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: Posts in this series:ArangoML Part 1: Where Graphs and…

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ArangoML Part 3: Bootstrapping and Bias-Variance

Christopher Woodward — Sat, 24 Oct 2020 08:22:00 +0000

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: Posts in this series:ArangoML Part 1: Where Graphs and Machine Learning MeetArangoML Part 2: Basic Arangopipe WorkflowArangoML Part 3: Bootstrapping and Bias VarianceArangoML Part 4: Detecting Covariate…

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ArangoML Part 2: Streamlining Machine Learning Workflows

Christopher Woodward — Fri, 09 Oct 2020 08:09:00 +0000

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 Posts in this series: ArangoML Part 1:…

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ArangoML Part 1: Where Graphs and Machine Learning Meet

Christopher Woodward — Fri, 25 Sep 2020 07:53:00 +0000

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. If you have a use case you would like to…

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ArangoML Pipeline Cloud – Managed Machine Learning Metadata Service

Joerg Schad — Thu, 30 Jan 2020 04:35:04 +0000

Estimated reading time: 4 minutes We all know how crucial training data for data scientists is to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important. Consider for example: Capture of Lineage Information (e.g., Which dataset influences which Model?) Capture of Audit Information (e.g, A given model was trained two…

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