ArangoDB Newsletter #132: April Updates and Insights

Hello Community, Welcome to the fourth ArangoDB newsletter of 2021! In this edition, we share details about: our latest and greatest lunch breaks, part four of our ArangoML blog series, as well as a guest article featured in DZone about the C++ memory model. We hope you enjoy!

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ArangoDB Newsletter #131: March Updates and Insights

We’ve officially entered Spring this month, so perhaps it’s time for a little spring cleaning? Here’s a few suggestions from us:  Optimize your AQL queries by checking out our latest lunch breaks  Learn something new with our ArangoML blog series Anything else you’d like to learn about? Drop us a line anytime at community [at] arangodb [dot]…

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ArangoDB Newsletter #130: February Updates and Insights

Hello community! This month we post on the blog bearing gifts up upcoming events, content we think you might enjoy, as well as suggestions around how to get involved with the community. Read on! We’d also love to hear from *you*! Drop us a line anytime at community [at] arangodb [dot] com.

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C++ Memory Model: Migrating from x86 to ARM

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…

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ArangoDB Newsletter #129: Stay Informed with the Latest Updates

Happy New Year to our amazing community! We hope your year is off to a good start. Even though things don’t seem too different (yet), we’re definitely feeling a fresh mindset and doing our best to stay hopeful and healthy.  Here’s what we’re excited about so far: Our new ‘Graph & Beyond’ Lunch Break series…

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ArangoML Series: Multi-Model Collaboration

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:

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State of the Art Preprocessing and Filtering with ArangoSearch

Just in case you haven’t heard about ArangoSearch yet, it is a high-performance Full-Text Search engine integrated in ArangoDB (meaning connected with the other data-models and AQL). Feel free to check out ArangoSearch – Full-text search engine including similarity ranking capabilities for more details. In ArangoDB version 3.7 the ArangoSearch team added Fuzzy Search support…

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ArangoML Series: Intro to NetworkX Adapter

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

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:

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ArangoML Part 3: Bootstrapping and Bias-Variance

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:

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