ArangoDB Events
Check out the upcoming ArangoDB Events, including Webinars, Meetups, Conference Talks and more.
Upcoming events

ArangoDB Webinar
Webinar: ArangoDB 3.11
May 31st, 2023 – 11:00 AM PDT / 1:00 PM CDT / 2:00 PM EDT
Shhhh, our 3.11 release is just around the corner! Join CTO Jörg Schad on May 31st, 2023, to learn more about our exciting new release first hand. RSVP today to save your spot!

ML Con Munich
Speaking Session: GraphML – The Next Level Machine Learning
June 21st, 2023 – 8:15 AM PST / 10:15 AM CST / 11:15 AM EST / 17:15 CEST
This presentation delves into why Graphs have become a major trend in Machine Learning. The discussion begins by examining Graph Analytics and its uses in areas such as Fraud and Anomaly Detection, Page Rank, Recommendation Systems, text summarization, and other NLP tasks.
Then, we will delve into Graph Machine Learning, exploring concepts such as embeddings and Graph Neural networks. Finally, we will take a step back and consider when it is appropriate to use these techniques compared to other methods. By the end of this session, attendees will have a deeper understanding of Graph Machine Learning techniques, their applications, and when to consider alternative options.

ML Con Munich
Workshop Session: Graph Powered Machine Learning
June 22nd, 2023: 9:00 – 17:00 CEST
From graph analytics to graph neural networks: Making the most of your graph data. In this workshop, you will gain hands-on experience with the latest topics in Analytics and Machine Learning: Graph Powered Machine Learning.
Graph Analytics has long demonstrated that it solves real-world problems, including Fraud, Ranking, Recommendation, text summarization, and other NLP tasks. More recently, Graph Machine Learning applied directly to graphs using graph algorithms, and machine learning has demonstrated significant advantages in solving the same problems as graph analytics and problems that are impractical to solve using graph analytics.
Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embedding and Graph Neural Networks that are used to complex problems in a different ways. We will cover Graph Basics, Graph Analytics, and Graph Machine Learning with many hands-on experiences.
Big Data London

Come Say Hi to ArangoDB!
September 20th – 21st, 2023 – London, England.
We are a Silver sponsor at Big Data LDN (London), the UK’s leading free-to-attend data & analytics conference and exhibition, hosting leading data and analytics experts, ready to arm you with the tools to deliver your most effective data-driven strategy. Visit us in booth 338!
Past events

ArangoDB Webinar
Webinar: Next-gen Network Management with Performance, Availability, and Security using ArangoDB
May 24th, 2023 – 11:00 AM PDT / 1:00 PM CDT / 2:00 PM EDT
The networking world is rapidly evolving. People are realizing that the traditional databases were not purpose-built to handle the array of complex connections that make up the enterprise stack. Graph Databases can be a huge enabler for the complexity of legacy systems, and ArangoDB has Graph, Document, and Search capabilities combined seamlessly to provide a Next Generation architecture.
Join us for this webinar with HPE where they will share how they achieved next-level performance using ArangoDB.
Powered by ArangoDB, HPE Aruba Networking Central is a cloud-based networking solution that empowers IT with AI-powered insights, intuitive visualizations, workflow automation, and edge-to-cloud security to manage campus, branch, remote, data center, and IoT networks from one dashboard.
What you will learn:
- The challenges HPE faced with traditional databases and infrastructure
- Why HPE turned to ArangoDB for improved network management and performance solutions
- The benefits HPE achieved using ArangoDB for network management

Meetup
Speaking Session: Thinking outside of the Euclidean Space: Graph Machine Learning with Fastgraphml
May 17th, 2023, 19:00 CET
Graph Machine Learning (ML) is a rapidly growing area of machine learning. It has attracted many people from various domains, including social science, chemistry, biology, physics, and e-commerce. Given the rise of this fascinating field, we developed fastgraphml package (built on top of PyG) that can help users to build Graph ML models with just three lines of code. In addition, the framework uses ArangoDB (the next-generation graph data and analytics platform) as a backend to export graphs directly into the fastgraphml package. Therefore, in this talk, I will talk briefly about recent trends in GraphML and how one can build GraphML models quickly using fastgraphml.

ArangoDB Webinar
Graph and Beyond: What and Why Graph?
April 26th, 2023 – 11:00 AM PDT / 1:00 PM CDT / 2:00 PM EDT
Graphs have become increasingly popular across various fields in recent times. In the first segment of our “Graph Done Right” series, we will explore the factors that make graphs a potent tool and their suitability for different applications. Our discussion will revolve around topics such as graph databases, graph analytics, and graph machine learning, and we will also explore the interconnectivity among these domains.

ArangoDB Webinar
Cyber Security at Finite State with ArangoDB
April 21st, 2023 – 8:30 AM PDT / 10:30 AM CDT / 11:30 AM EDT
In this presentation, we will discuss how Finite State uses ArangoGraph Insights Platform to address cyber threats in OT and IoT environments. We will cover the increasing sophistication of cyber attacks and the challenges organizations face in defending against them. We will then introduce using ArangoDB’s graph database architecture as a powerful tool for analyzing and visualizing complex data related to cyber threats.
A live demo showcasing ArangoGraph Insights Platform’s capabilities will be presented, followed by a Q&A session. Overall, this presentation will provide an overview of the impact of cyber threats on organizations and how ArangoGraph Insights Platform can be used to mitigate these threats.

ArangoDB Webinar
Fraud Detection with ArangoDB
March 15th, 2023 – 11 AM PT / 1 PM CT / 2 PM ET
Across industries, fraud is a growing problem resulting in a global annual loss of $3.7 trillion. Fraudsters became more sophisticated in hiding their activities by forming fraud rings and using stolen identities and other patterns. Traditional approaches still focus on discrete data missing many opportunities to identify or prevent fraud.
In this webinar, join Solution Architect Victor Moey as he showcases how to convert data from relational to graph, how fraud detection queries work in ArangoDB’s Query Language (AQL), and how fraud detection can be done at scale.

ArangoDB Webinar
fastgraphml with ArangoDB
February 15th, 2023 – 11 AM PT / 1 PM CT / 2 PM ET
Graph Machine Learning has attracted a large number of people from various domains ranging from social science to chemistry, biology, physics, and e-commerce. Given the rise of this fascinating field, we are proud to release the fastgraphml package (built on top of PyG) that can help users to build Graph ML models with just 3 lines of code. In addition, the framework uses ArangoDB (ArangoGraph Insights Platform) as a backend to export graphs directly into the fastgraphml package.

ArangoDB Webinar
AQL in 2023
January 18th, 2023 – 11 AM PT / 1 PM CT / 2 PM ET
Join us for our “New Year, New AQL” webinar to make 2023 the year you become an AQL master. You will learn how to get started with AQL and about some of the exciting new AQL features and functions introduced in 2022.
In this webinar, Chris Woodward and Jon Schuback help you with getting started with AQL by introducing some of the fundamentals and showing off the wide range of functions available. You’ll not leave empty-handed either; we’ve prepared a learning plan to help you stay on track to become an AQL pro in 2023, free for all attendees!
O'Reilly Live Training

Jörg Schad “Graph Powered Machine Learning First Steps”


Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.
Join expert Jörg Schad to explore the symbiosis of graphs and machine learning, starting with graph analytics to graph neural networks. You’ll learn why graphs are such a powerful abstraction and discover how to leverage them in your machine learning projects.

ArangoDB Webinar
Dots and Lines – Graph Done Right with ArangoDB
December 7th, 2022 – 11 AM PT / 1 PM CT / 2 PM ET
Datapoints are valuable and connected data points even more so. Modeling your data as a Graph unlocks knowledge you did not know you had before. In this webinar with graph expert Markus Pfeiffer, we will give you a tour of ArangoDB’s graph capabilities and learn about Graph traversals, shortest paths, smart graphs, and more! Register today to secure your spot
MLCon Berlin 2022

Jörg Schad “Graph-Powered Machine Learning Workshop”
December 1st, 2022
From graph analytics to graph neural networks: Making the most of your graph data. In this workshop, you will gain hands-on experience with the latest topics in Analytics and Machine Learning: Graph Powered Machine Learning. Graph Analytics has long demonstrated that it solves real-world problems, including Fraud, Ranking, Recommendation, text summarization, and other NLP tasks. More recently, Graph Machine Learning applied directly to graphs using graph algorithms and machine learning has been demonstrating significant advantages in solving the same problems as graph analytics as well as problems that are impractical to solve using graph analytics. Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embedding and Graph Neural Networks that are used to complex problems in a different way. We will cover Graph Basics, Graph Analytics, and Graph Machine Learning with many hands-on experiences.
Toronto Machine Learning Summit

Jörg Schad “GraphML – The Next Level of Machine Learning”
November 28th, 2022
Many Machine Learning fail to turn an initial idea and potentially even the first model into a business impact, as they neglect the importance (and associated work) of building a production-grade ML pipeline. There are many great tutorials for training your deep learning models using PyTorch, TensorFlow, Keras, Spark, or one of the many other frameworks. But training is only a small part of the overall deep learning pipeline. This workshop gives an overview into building a complete automated deep learning pipeline starting with exploratory analysis, over training, model storage, model serving, meta-data storage, and monitoring using available Open-Source tool.
Big Data Conference Europe 2022

Christopher Woodward “Machine Learning + Graph Databases for Better Recommendations”
November 24th, 2022 3:30pm CET/ 6:30am PDT/ 9:30am EDT
This talk will cover topics related to providing relevant recommendations to users. We don’t aim to declare one recommendation method as the best but instead highlight different approaches to enriching recommendations by combining machine learning with graph databases.
The methods we evaluate include:
– Matrix Factorisation with Graph Embeddings
– Content-based TFIDF
– Cosine Similarity with AQL and User Ratings

ArangoDB Webinar
kube-arangodb
November 10th, 2022 – 11 AM PT / 1 PM CT / 2 PM ET
In this webinar, ArangoDB Software Engineer Jakub Wierzbowski will share how to extend Kubernetes API for custom ArangoDB objects and spin up the ArangoDB database in cluster mode. He will first walk you through how to deploy kube-arangodb operator to control the entire lifecycle and discuss key features, including scaling, upgrading, ARM64 migration, (and many more!) of that operator in both: Community and Enterprise modes.
All Things Open Conference 2022

Christopher Woodward “Machine Learning + Graph Databases for Better Recommendations”
November 01st, 2022 6:45pm CET/ 10:45am PDT/ 1:45pm EDT
This talk will cover topics related to providing relevant recommendations to users. We don’t aim to declare one recommendation method as the best but instead highlight different approaches to enriching recommendations by combining machine learning with graph databases.
The methods we evaluate include:
- Matrix Factorization with Graph Embeddings
- Content-based TFIDF
- Cosine Similarity with AQL and User Ratings
The talk will briefly cover the methods and how we generated the distance metrics and provide notebooks that go into further detail. We will show how we integrated these findings into a frontend application for movie recommendations. The talk aims to show how pairing machine learning with graph databases can improve the quality of recommendations and offers some insights into the challenges of productionizing machine learning models.
KubeCon + CloudNativeCon Detroit

Come Say Hi to ArangoDB!
October 24th – 28th, 2022 – Detroit, Michigan.
We are a Silver sponsor at KubeCon Detroit, the flagship event for adopters and technologists from leading open source and cloud native communities. Stop by our in-person and virtual booths to learn more!

ArangoDB Summit
ArangoDB Summit 2022
October 4th – 5th, 2022
Join Team Avocado for our very first 2-day industry event. This event will include a variety of talks, workshops, and conversations about ArangoDB and the Graph Database Industry. Get inside knowledge on ArangoDB, and learn about some of our latest and coolest use cases before anyone else. Be ready to meet other brilliant minds, exchange ideas, network, and lay the foundation for new, fruitful partnerships thanks to our Networking area. We hope to see you online soon!
Big Data London

Come Say Hi to ArangoDB!
September 21st – 22nd, 2022 – London, England.
We are a Silver sponsor at Big Data LDN (London), the UK’s leading free-to-attend data & analytics conference and exhibition, hosting leading data and analytics experts, ready to arm you with the tools to deliver your most effective data-driven strategy. Visit us in booth 338!

ArangoDB Webinar
Challenges of a Cloud Native Database
September 14th, 2022 – 11 AM PT / 1 PM CT / 2 PM ET
Do you remember the days when your Postgres process (or favorite stateful application) had an uptime of 700 days or more? Today databases have moved from dedicated servers in some basement into the cloud (and often run inside Kubernetes). This ‘cloud-native’ move is not just about operations but is fundamental to architectural decisions (or changes), especially for stateful applications (in our case, an open-source distributed graph database).
In this talk, our CTO, Jörg Schad, Ph.D., will share his journey from building a ‘traditional’ database system to the experiences the team has made building ArangoDB Cloud, the managed service behind ArangoDB. The focus will be on lessons learned and best practices ranging from the core architecture and cloud platform to the team structure.

ArangoDB Webinar
What You Always Wanted to Know About Clusters but Were too Afraid to Ask.
August 24th, 2022 – 11 AM PT / 1 PM CT / 2 PM CT
Following best practices for running ArangoDB clusters is generally not complicated once it is clear if clustering is required for safety, performance, or both. Resources then need to be allocated, and within no time, the cluster is up and running. This is impossible without monitoring and the ability to verify usage assumptions and identify weak links. Join us for this webinar, where we will address all aspects of a responsible and dependable operation of ArangoDB clusters, large and small.
FrOSCon

Christopher Woodward “Machine Learning + Graph Databases for Better Recommendations”
August 21st, 2022, 14:00 PM CEST
This talk will cover topics related to providing relevant recommendations to users. We don’t aim to declare one recommendation method as the best but instead highlight different approaches to enriching recommendations by combining machine learning with graph databases.
The methods we evaluate include:
– Matrix Factorization with Graph Embeddings
– Content-based TFIDF
– Cosine Similarity with AQL and User Ratings
The talk will briefly cover the methods and how we generated the distance metrics and provide notebooks that go into further detail. We will show how we integrated these findings into a frontend application for movie recommendations. The talk aims to show how pairing machine learning with graph databases can improve the quality of recommendations and offers some insights into the challenges of productionizing machine learning models.
Beer City Code 2022

Christopher Woodward “Machine Learning + Graph Databases for Better Recommendations”
August 6th, 2022 11:00pm CEST / 2:00pm PDT/ 5:00pm EDT
This talk will cover topics related to providing relevant recommendations to users. We don’t aim to declare one recommendation method as the best but instead highlight different approaches to enriching recommendations by combining machine learning with graph databases.
The methods we evaluate include:
– Matrix Factorization with Graph Embeddings
– Content-based TFIDF
– Cosine Similarity with AQL and User Ratings

'Graph & Beyond The Second Course' Lunch Break
#2.12: What is a Graph Database
August 3rd, 2022 – 12 PM Local Time
Graphs occur everywhere in everyday life: your network of friends, the network of roads you drive on, and the supply chain of factories, ships, and roads that brought you the device you’re reading this on. Graph databases themselves, are the hottest thing in the database industry. Join our Senior Digital Marketing Manager, Laura Cope as she explores:
- What is a graph?
- What is a graph database?
- Different types of graph databases.
- Graph database use cases.

'Graph & Beyond The Second Course' Lunch Break
#2.11: ArangoRDF
July 20th, 2022 – 12 PM Local Time
This lunch session will introduce ArangoRDF an RDF adapter developed with the community as a first step at bringing RDF graphs into ArangoDB. The adapter is still in early development and we are hoping to build out its features based on community feedback.

'Graph & Beyond The Second Course' Lunch Break
#2.10: ArangoSearch (Advanced Analytics)
July 6th, 2022 – 12 PM Local Time
This lunch session serves as the next part in the ArangoSearch series of videos and we will focus on ArangoSearch on Graphs. In this video, Chris Woodward walks us through some more complex queries that combine graph traversals and ArangoSearch features to help take your search expertise to the next level.
ML Summit 2022

Jörg Schad “Graph Powered Machine Learning”
June 29th 5:45pm CEST / 8:45am PDT/ 11:45am EDT
In this two part workshop, you will gain hands-on experience with the latest topics in Analytics and Machine Learning: Graph Powered Machine Learning. Graph Analytics has long demonstrated that it solves real-world problems, including Fraud, Ranking, Recommendation, text summarization, and other NLP tasks. More recently, Graph Machine Learning applied directly to graphs using graph algorithms and machine learning has been demonstrating significant advantages in solving the same problems as graph analytics as well as problems that are impractical to solve using graph analytics. Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embedding and Graph Neural Networks that are used to complex problems in a different way.
Kubernetes Community Days Berlin 2022

Jörg Schad “Don’t Panic: Challenges in Building and Operating a Multi-Cloud-Provider Platform”
June 29th 6:15pm CEST / 9:15am PDT/ 12:15pm EDT
Building a cloud-agnostic platform used to be a challenging task as one had to deal with a large number of different cloud APIs and service offerings. Today, as most Cloud providers are offering a managed Kubernetes solution (e.g., GKE, AKS, or EKS), it seems like developers could simply build a platform based on Kubernetes and be cloud-agnostic. While this assumption is mostly correct, there are still a number of differences and pitfalls when deploying across those managed Kubernetes solutions. This talk discusses the experiences made while building the ArangoDB Managed Service offering across and GKE, AKS, or EKS. While the (managed) Kubernetes API being a great abstraction from the actual cloud provider, a number of challenges remain including for example networking, autoscaler, cluster provisioning, or node sizing.
BeyondGraph 22

ArangoDB BeyondGraph 22
June 28th 3:00pm CEST / 6:00am PDT/ 9:00am EDT
DevDays is now BeyondGraph. Mark your calendars for Tuesday, June 28th. BeyondGraph22 is coming to a computer screen near you! Join us for a day-long virtual conference with exclusive talks, engaging demos, insightful workshops, and invaluable networking opportunities. Don’t miss your chance to learn about the latest updates from ArangoDB, connect with fellow Community members, and get a sneak peek at some exciting new things we are working on.
O'Reilly Live Training

Jörg Schad “Graph Powered Machine Learning First Steps”


Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.
Join expert Jörg Schad to explore the symbiosis of graphs and machine learning, starting with graph analytics to graph neural networks. You’ll learn why graphs are such a powerful abstraction and discover how to leverage them in your machine learning projects.

'Graph & Beyond The Second Course' Lunch Break
#2.9: Introducing the ArangoDB-NetworkX Adapter
June 22nd, 2022 – 12 PM Local Time
This lunch session will walk you through using our NetworkX adapter and how to easily convert ArangoDB graphs to NetworkX graphs and back again! See how using this adapter gives you the best of both graph worlds with all of the speed and flexibility of ArangoDB and the ubiquity of NetworkX.
Data Day Texas 2022

Arthur Keen “Machine Learning, Semantics, and Knowledge Graphs”
June 13th, 2022 6:40pm CEST/ 9:40am PDT/ 12:40pm EDT
This talk describes a story of lessons learned in a journey that started with the objective of developing an application that required integration of a knowledge graph with multiple machine learning models, which rapidly encountered the hard reality of impedance mismatches between the technologies, and how these differences were addressed using semantic models. Knowledge graphs and graph machine learning seem like a perfect match, though in practice there are subtle differences between the two domains that can cause friction. By analogy, the ideal of the knowledge graph is the perfect crystalline structure of a diamond representing everything that is known about a domain in a logical way, whereas machine learning values flexibility of models trained on a subset of the data that can stretch like rubber to accommodate data never encountered before. The knowledge graph and machine learning communities use different approaches to data transformation and problem solving. They have different key performance metrics. They use dissimilar graph structures and vocabularies. They use different forms of inference, and they deal with imprecision in different ways. The talk describes these differences and ways to address them in a systematic way.
Data Day Texas 2022

Sachin Sharma “Graph Neural Networks with PyTorch Geometric and ArangoDB”
June 13th, 2022 11:20pm CEST/ 2:20pm PDT/ 5:20pm EDT
So far we have read a lot of articles regarding the Convolutional Neural Networks which is a well-known method to handle euclidean data structures (like images, text, etc.). However, in the real world, we are also surrounded by non-euclidean data structures like graphs, and a machine learning method to handle this type of data domain is known as the Graph Neural Networks. Therefore in this workshop, we will first deep dive into the concepts of Graph Neural Networks and their Applications (Part-1), and then during the Hands-on-Session (Part-2), we will build a Graph Neural Network application (with Pytorch Geometric) and ArangoDB.
Data Day Texas 2022

Jörg Schad “It was the best of Graph, it was the worst of Graph – Choosing between Graph ML and Graph Analytics”
June 13th, 2022 9pm CEST/ 12pm PDT/ 3pm EDT
Graph Analytics has long demonstrated that it solves real-world problems, including Fraud, Ranking, Recommendation, text summarization, and other NLP tasks. More recently, Graph Machine Learning applied directly to graphs using graph algorithms, and machine learning, has been demonstrating significant advantages in solving the same problems as graph analytics as well as problems that are impractical to solve using graph analytics. Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embedding and Graph Neural Networks that are used to complex problems in a different way. In this talk, we will compare and contrast these two approaches (spoiler: often complexity vs precision) in real-world scenarios. What factors should you consider when choosing one over the other and when do you even have a choice? Join this talk to learn about exciting new developments in Graph ML and especially when not to use tem.

'Graph & Beyond The Second Course' Lunch Break
#2.8: Introducing the ArangoDB-DGL Adapter
June 08th, 2022 – 12 PM Local Time
This lunch session introduces you to our DGL adapter. We walk through the base examples of how to use the adapter to export ArangoDB graphs to DGL. The Deep Graph Library (DGL) is an easy-to-use, high-performance, and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logic can be implemented in any major frameworks, such as PyTorch, Apache MXNet, or TensorFlow.
Machine Learning Prague 2022

Sachin Sharma “Thinking outside of the Euclidean Space: An introduction study to Graph Machine Learning and its Applications”
May 28th, 2022 11 AM CEST
So far we have heard a lot about Convolutional Neural Networks (CNNs) which is a well-known method to handle euclidean data structures (like images, text, speech, and time series). However, in the real world, we are also surrounded by non-euclidean data like graphs, and a machine learning method to handle this type of data is known as the Graph Neural Networks. Therefore, in this conference, we will deepen our knowledge with the concepts of Graph Neural Networks and its applications in various domains.

'Graph & Beyond The Second Course' Lunch Break
#2.7: Recommendation Demo on Oasis
May 25th, 2022 – 12 PM Local Time
For this lunch session, we recommend leaving your head in the clouds as we go through our ArangoDB Oasis recommendation engine demo. Our ML developer relations engineer Chris Woodward will introduce you to ArangoFlix, a movie streaming site that shows an example of combining machine learning with a graph database. We show how to offer the most chill-worthy movies for your next watching session.
KubeCon + CloudNativeCon Europe 2022

Transparent Live Migration of Services Between Kubernetes Cluster – Adam Janikowski & Jörg Schad, ArangoDB
May 19th, 2022 03:25 PM CEST
Operating a distributed database on a single Kubernetes cluster is interesting, but how about transparently migrating it from one cluster to another–potentially between different cloud providers– without impacting user workloads? Kubernetes has become the de facto default deployment for ArangoDB, a distributed Graph database. Consider for example ArangoDB Oasis, a managed Cloud Database service with over 200 deployments (aka highly available database clusters) across three major cloud providers and many regions. But outages, (Kubernetes) upgrades, resource considerations, and cost optimizations require the underlying infrastructure to be very dynamic including migration between Kubernetes cluster, datacenter, or even cloud providers. This talk provides insights into how Kube-Arango, the OSS operator for ArangoDB, supports live migration of distributed stateful applications without impact on users. Challenges in such migration include for example networking, DNS, and persistent data.

'Graph & Beyond The Second Course' Lunch Break
#2.6: Graph Embeddings with AQL
May 11th, 2022 – 12 PM Local Time
In this session, Sachin will put more light on this rapidly growing area and its industrial and research applications. In addition to this, he will also demonstrate how we can leverage graph embeddings with ArangoDB’s AQL query language. The key idea of this session would be to generate Amazon Product Recommendations with the help of ArangoDB’s AQL query language.We are going to follow this github repository for our session.
O'Reilly Live Training

Jörg Schad “Graph Powered Machine Learning First Steps”


Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.
Join expert Jörg Schad to explore the symbiosis of graphs and machine learning, starting with graph analytics to graph neural networks. You’ll learn why graphs are such a powerful abstraction and discover how to leverage them in your machine learning projects.

'Graph & Beyond The Second Course' Lunch Break
#2.5: Community vs. Enterprise
April 20th, 2022 – 12 PM Local Time
Curious about how to improve performance, business continuity, and security across your No-SQL database? Turning your project into a full-blown production application? Join us during the upcoming Lunch and Learn on (DATA) to understand more about ArangoDB about ArangoDB’s unique capabilities with our Enterprise Edition capabilities.
MRC Data Sciences Summit

Jörg Schad Keynote: Next-Generation Machine Learning: The Power of Graph
April 6th, 2022 6 AM PT/ 3 PM CET
The next generation of Machine Learning is based on graphs enabling it to incorporate relationships inside data explicitly. Already, many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.

'Graph & Beyond The Second Course' Lunch Break
#2.4: Graph vs. Relational
April 6th, 2022 – 12 PM Local Time
Join us for our next Lunch and Learn session: Graph vs. Relational. In this upcoming recording, our Account Executive Enzo Zenuni will explore the differences between relational databases and graph databases.

'Graph & Beyond The Second Course' Lunch Break
#2.3: Recommendation Engine
March 23rd, 2022 – 12 PM Local Time
In this Lunch & Learn, we give an overview of the various approaches for recommending products/services as well as demo how these approaches can be implemented using both Search and Graph in ArangoDB.

'Graph & Beyond The Second Course' Lunch Break
#2.2: How to Contribute
March 9th, 2022 – 12 PM Local Time
The fact that ArangoDB is open source isn’t really something we have kept a secret. However, the process for how to contribute isn’t always as clear. In this lunch and learn session Chris walks you through all the ways that you can contribute and the steps involved. You will see what it takes to contribute to the core ArangoDB repositories and even how to get started with our amazing community-driven projects!
Webinar

Bluwr.com: Using Blumycellium automation tools with ArangoDB backend

Bluwr.com is a text-based social publication platform that aims at efficiently servicing millions of users. Here we introduce how we are using ArangoDB as the unique database for the project and we will look at Blumycellium, a lightweight library we developed for async communication between processes that uses ArangoDB as a back end. Blumycellium is one of our main automation tools for Bluwr and will be open-sourced from the get-go.
ArangoDB Webinar

ArangoDB 3.9 Release ft. CTO Jörg Schad

It was a long journey but the team is proud to announce the release of ArangoDB 3.9! Join CTO Jörg Schad as we celebrate the latest ArangoDB 3.9 release.

'Graph & Beyond The Second Course' Lunch Break
#2.1: Hybrid SmartGraphs
February 23rd, 2022 – 12 PM Local Time
In this Lunch and Learn Session Chris will introduce you to Hybrid SmartGraphs! The first course will include a brief introduction to SmartGraphs. For the entree, we will take a look at the new Hybrid SmartGraph and how to create them. Finally, for the dessert, we will even see how to use AQL to post-process data for SmartGraphs!
ArangoDB Community Townhall

ArangoDB Community Townhall ft. CTO Joerg Schad

Join the ArangoDB community for an interactive gathering with our CTO Joerg Schad. Our quarterly community town halls are an opportunity to stay up to date with the latest ArangoDB news, get a preview of what’s coming next, and provide us with feedback on how to continue improving ArangoDB for you. There will be a virtual networking lounge after Joerg’s session so feel free to stick around and get to know your community!
Webinar

Introdução ao ArangoDB através do Python (Português)

Neste webinar discutiremos conceitos básicos de bancos de dados NoSQL e faremos uma aplicação prática da solução ArangoDB usando sua API para Python. ArangoDB é uma solução NoSQL que trabalha com diferentes formas de dados com grafos, documentos e pode ser usada on premise ou em nuvem.
Webinar

How to Set Up ArangoDB Oasis for Success

This webinar will focus on how to get started with ArangoDB Oasis, making sure you actively use the first 14 days of your free trial to help you decide the next course of action.
Join Yaman Srivastava, Oasis Customer Success Manager, January 19th, 2022 8 AM PST / 11 AM EST / 5 PM CET.
ML Summit 2021

Jörg Schad “Graph Powered Machine Learning: Part 1 and Part 2”
December 8th, 2021 2 AM PDT / 5 AM EDT / 11 AM CEST
In this two part workshop, you will learn about Graph Powered Machine Learning starting with simple graph algorithms, over graph analytics, up to Graph Neural Networks.
In particular, the workshop will cover the following topics:
– Why graphs are such a powerful abstraction
– What use-cases are suitable for Graph-based Machine Learning
– How to leverage knowledge graphs
– The graph ecosystem including its many many powerful open source tools
– How to extract value from graphs using graph analytics and graph algorithms
– How to combine deep learning and graphs
– How we can learn graphs features using graph neural networks
Connected Data World 2021

Jörg Schad “Graph Analytics vs Graph ML”
December 3rd, 2021 04:20 PM CET
In this talk, we will compare and contrast these two approaches (spoiler: often complexity vs precision) in real-world scenarios. What factors should you consider when choosing one over the other and when do you even have a choice? Join this talk to learn about exciting new developments in Graph ML, as the graph techniques on which they are based.
Codemotion English Edition 2021

Sachin Sharma “Thinking outside of the Euclidean Space: An introduction study to Graph Machine Learning and its Applications”
November 30th, 2021 14:00 PM CET/ 05:00 AM PDT
So far we have heard a lot about Convolutional Neural Networks (CNNs) which is a well-known method to handle euclidean data structures (like images, text, speech, and time series). However, in the real world, we are also surrounded by non-euclidean data like graphs, and a machine learning method to handle this type of data is known as the Graph Neural Networks. Therefore, in this workshop, we will deepen our knowledge with the concepts of Graph Neural Networks and compare them with CNNs. This will be followed by an interactive Hands-On Session on Graph ML with a real-world application dataset. At the end, we will deploy our generated Graph ML model on the Triton inference server and perform Graph Analytics with the help of ArangoDB.
Big Things Conference 2021

Jörg Schad “Production-grade ML Pipelines – From Data To Metadata”
November 18th, 2021 15:30 PM CET
Modern Machine Learning platforms contain a number of different components: Distributed Training, Jupyter Notebooks, CI/CD, Hyperparameter Optimization, Feature stores, and many more. Most of these components have associated metadata including versioned datasets, versioned Jupyter Notebooks, training parameters, test/training accuracy of a trained model, versioned features, and statistics from model serving. For the dataops team managing such production platforms, it is critical to have a common view across all this metadata, as we have to ask questions such as: Which Jupyter Notebook has been used to build Model XYZ currently running in production? If there is new data for a given dataset, which models (currently serving in production) have to be updated? In this talk, we look at existing implementations, in particular, MLMD as part of the TensorFlow ecosystem.

'Graph & Beyond' Lunch Break
#23 – Advanced Aggregation Queries with AQL
November 17th, 2021 – 12 pm
Building on the previous lunch session, we will explore more variants of the COLLECT operation. Instead of grouping full documents, you can use a subset of attributes. Using multiple COLLECT operations is another interesting use case. We will also delve into COLLECT with AGGREGATE as an efficient way to compute statistical properties during grouping.
C++ Russia 2021

Valery Mironov “Deep dive into Futures: Task Parallelism approach”
November 15th, 2021 16:30 PM CET / 18:30 PM MSK/ 07:30 AM PDT
The talk will cover the following topics:
- Why are most of the in-house developments so similar to Futures in terms of API?
- What API should Futures have, and how can it be most efficiently implemented?
- Many different optimizations: reducing the number of locks, false awakenings, allocations.

'Graph & Beyond' Lunch Break
#22 – Basic Aggregation Queries with AQL
November 3rd, 2021 – 12 pm
The COLLECT operation in ArangoDB’s query language AQL is a versatile tool. It lets you group records based on attribute values but also deduplicate values, count how often each value occurs, and more. In this lunch session, we will take a look at the essential variants of COLLECT.
Dev Days 2021

ArangoDB Dev Days 2021
October 18th- 22nd, 2021
ArangoDB is thrilled to announce our first-ever virtual developer conference. Featuring talks, fireside chats, demos, and exclusive access workshops. Join us and take part in the future of data science and machine learning. Learn about the latest best practices, tools, techniques and connect with ArangoDB’s dynamic community.
Register for open access to the five-day event. Workshops are limited seating and require additional registration, which can be found below the open access sign-up.

'Graph & Beyond' Lunch Break
#21 – Graph Embeddings
October 20th, 2021 – 12 pm
In this session, Sachin Sharma will put more light on this rapidly growing area and its industrial applications. In addition to this, he will also demonstrate node embeddings generated from Amazon product co-purchasing network which can be used to predict shopping preferences.

'Graph & Beyond' Lunch Break
#20 – Movie Search Demo
October 6th, 2021 – 12 pm
This guide will get you started with ArangoSearch, the ArangoDB search engine, by providing example queries of some useful AQL functions. The International Movies Database(IMDB) dataset is pre-loaded with the installation of this example, along with the necessary ArangoSearch View(firstView). Sit down and get ready for Jackson Reimers to take you through our Movie Search Demo, available on ArangoDB Oasis.
Webinar

Exploring Abstract Syntax Trees with ArangoDB

Sarah Henkens will discuss how Slack performs code analysis by modeling abstract syntax trees as a graph model in ArangoDB. Learn how to design effective graph data models and how to utilize AQL to traverse an AST.
Join us on September 29th 9 AM PDT / 12 PM EDT / 6 PM CEST

'Graph & Beyond' Lunch Break
#19 – Hot Backups and Restores in ArangoDB
September 22nd, 2021 – 12 pm
Database servers are entrusted with oftentimes vital data. ArangoDB offers all the bells and whistles in the book to provide the utmost consistency and safety for your data. But no matter how much energy one invests in creating a safe environment, having the facility to create consistent, affordable, and yet effortless snapshots of a data operation is invaluable. This talk walks you through the range of possibilities and highlights pros and cons. Most of the demonstration is done on a live ArangoDB cluster.
Workshop

Graph ML, NVIDIA Triton, and ArangoDB: Thinking Beyond Euclidean Space

So far we have heard a lot about Convolutional Neural Networks (CNNs) which is a well-known method to handle euclidean data structures (like images, text, speech, and time series). However, in the real world, we are also surrounded by non-euclidean data like graphs, and a machine learning method to handle this type of data is known as the Graph Neural Networks. Therefore, in this workshop, we will deepen our knowledge with the concepts of Graph Neural Networks and compare them with CNNs.
Online Meetup

Using Multi-Model for Machine Learning Metadata

In this talk, the ArangoDB ML Developer Relations Engineer Chris Woodward will walk you through how to use ArangoML Pipeline to gather metadata from your pre-existing machine learning pipelines.

'Graph & Beyond' Lunch Break
#18 – RecallGraph
September 8th, 2021 – 12 pm
The talk will examine the importance of data-versioning, some related concepts, and the specific capabilities of RecallGraph itself. Finally, a quick demo with an application that leverages Recallgraph’s time-traveling feature

'Graph & Beyond' Lunch Break
#17 – Introduction to Foxx Microservices
August 25th, 2021 – 12 pm
Sit down with Chris Woodward as he shows you the ingredients that make Foxx work so well and then see how to cook up your first Foxx microservice in this ArangoDB Lunch Session.
FrOSCon 2021

Chris Woodward “Using GeoJSON Data in a Fullstack Vue Application”
August 21st, 2021 7 AM PDT / 10 AM EDT / 4 PM CEST
This talk will discuss the initial steps in developing a vacation rental application with VueJS, Koa, and ArangoDB. The talk covers what GeoJSON data is, how we used it in our application, and the technologies used for the VueJS frontend. We showcase how to use our built-in full-text search engine ArangoSearch for text information retrieval with the new GeoJSON analyzer.

'Graph & Beyond' Lunch Break
#16 – ArangoDB for Beginners
August 11th, 2021 – 12 pm
Join Digital Marketing Manager Laura Cope who will give a basic introduction to ArangoDB and the different services ArangoDB has to offer.

'Graph & Beyond' Lunch Break
#15.5 – Aggregating Time-Series Data with AQL
August 4th, 2021 – 12 pm
In this special 3.8 release lunch break session, we will take a look at the two syntax variants of the WINDOW operation and go over a few examples queries with visual explanations.

'Graph & Beyond' Lunch Break
#15 – Entity Resolution
July 28th, 2021 – 12 pm
In this lunch session, we show why a graph database is well-suited for Entity Resolution together with a demo in ArangoDB.

'Graph & Beyond' Lunch Break
#14 – Monitoring ArangoDB
July 14th, 2021 – 12 pm
Calling monitoring services best practice, especially those in production, is surely understated. Not only does one gain insight into resource utilisation and can thus responsibly and optimally adjust the environment for apparent needs, but one is put into the position to understand failures on the application as well as the service side.
ArangoDB exposes tons of metrics counters, values, and histograms. In this lunch session, learn how to set up and read monitoring on ArangoDB instances, and take a walk through of the most significant metrics and discuss alerting based on them.
Workshop

Serving AI Models at Scale with Nvidia Triton

In this workshop, join Machine Learning Research Engineer Sachin Sharma and learn how to use Nvidia’s Triton Inference server (formerly known as TensorRT Inference Server), which simplifies the deployment of AI models at scale in production. We focus on hosting/deploying multiple trained models (Tensorflow, PyTorch) on the Triton inference server to leverage its full potential for this examination. Once models are deployed, we can make inference requests and can get back the predictions.
Online Meetup

Community Pioneer: “Fast image search using ArangoDB and the Google Vision API”

Join Chris Woodward and community member Anthony Mahanna in this community pioneer webinar.
Here we discuss how Anthony leveraged ArangoDB’s multi-model capabilities along with the Google Vision API to build Picsum Vision.

'Graph & Beyond' Lunch Break
#13 – Kubernetes Meets Graphs
June 30th, 2021 – 12 pm
Kube-Arango is ArangoDB’s Kubernetes Operator, which by the way also powers ArangoDB’s managed Service Oasis.
We will cover the entire Lifecycle of an ArangoDB Cluster on Kubernetes, starting from deployment, over scaling, to upgrades. Furthermore, we will look into best practices such as Monitoring.
Webinar

ArangoDB 3.8 – Analytics at Scale

The ArangoDB community and team are proud to release the next version of ArangoDB, an open-source, highly scalable graph database with multi-model capabilities.
With version 3.8, ArangoDB doubles down on its capabilities for Analytics at scale, especially in the area of Graph and ArangoSearch including:
- AQL window operations
- Weighted Graph Traversal
- ArangoSearch Pipeline & AQL Analyzers
- Enhanced Geo support in ArangoSearch
and many more exciting features!
Join our CTO, Jörg Schad, Ph.D. in this webinar to learn more about ArangoDB 3.8 and the roadmap for upcoming releases.

'Graph & Beyond' Lunch Break
#12 – Knowledge Graphs
June 16th, 2021 – 12 pm
This lunch session is an introductory video and focuses on the general concepts of knowledge graphs; the video covers:
- What is a Graph Database?
- What is a Knowledge graph?
- How do you Build a Knowledge Graph?
AI@Enterprise Summit 2021

Jörg Schad “Production-grade ML Pipelines – From Data To Metadata”
June 9th, 2021 – 6:30 – 6:50 PM (CET)
It is well known that data quality and quantity are crucial for building Machine Learning models, especially when dealing with Deep Learning and Neural Networks. But besides the data required to build the model itself, there is another often overlooked type of data required to build a production-grade Machine Learning Platform: Metadata.
Modern Machine Learning platforms contain a number of different components: Distributed Training, Jupyter Notebooks, CI/CD, Hyperparameter Optimization, Feature stores, and many more. Most of these components have associated metadata including versioned datasets, versioned Jupyter Notebooks, training parameters, test/training accuracy of a trained model, versioned features, and statistics from model serving. For the dataops team managing such production platforms, it is critical to have a common view across all this metadata, as we have to ask questions such as: Which Jupyter Notebook has been used to build Model XYZ currently running in production? If there is new data for a given dataset, which models (currently serving in production) have to be updated? In this talk, we look at existing implementations, in particular, MLMD as part of the TensorFlow ecosystem.

'Graph & Beyond' Lunch Break
#11 – Fuzzy Search
June 2nd, 2021 – 12 pm
Fuzzy search is an umbrella term for approximate matching in text retrieval. A common application is to compensate for typos in search phrases. In this Lunch Session, we will take a look at different similarity measures and show how fuzzy search works in ArangoDB.
Data+AI Summit 2021

Jörg Schad “Graph-Powered Machine Learning”
May 26th, 2021 – 12:05 PM (PT)
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.

'Graph & Beyond' Lunch Break
#10 – Oasisctl: Providing full control of your Oasis Cluster
May 19th, 2021 – 12 pm
In this Lunch Break session, Site Reliability Engineer Marcin Swiderski will give an overview of Oasisctl, a tool created by the ArangoDB Oasis team in order to help automate some common Oasis tasks. You will learn what Oasisctl is, how to use it, and how you can leverage it in your projects. We will cover some common operations that can be automated or included in your CI/CD pipelines.
O'Reilly Live Training

Jörg Schad “Graph Powered Machine Learning First Steps”
Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.
Join expert Jörg Schad to explore the symbiosis of graphs and machine learning, starting with graph analytics to graph neural networks. You’ll learn why graphs are such a powerful abstraction and discover how to leverage them in your machine learning projects.

'Graph & Beyond' Lunch Break
#9 – ArangoML
May 5th, 2021 – 12pm
In this Graph & Beyond Lunch Session Jörg Schad will give an overview of different parts of the ML pipeline and how ArangoDB fits in. In particular, we will be talking about feature engineering, Graph ML, Embeddings, MLOps, and Metadata.
Spoiler: There will be a number of Jupyter notebooks allowing you to get hands-on experience!

'Graph & Beyond' Lunch Break
#8 – Introduction to ArangoBnB
April 21st, 2021 – 12pm
In this Lunch Break, Developer Relations Engineer Chris Woodward provides a peek at the ArangoBnB project, a Fullstack JavaScript Web App that is being developed to showcase the upcoming ArangoSearch GeoJSON features.
ML Summit 2021

Jörg Schad “Graph Powered Machine Learning”
April 20th, 2021
In this workshop, you will learn about Graph Powered Machine Learning starting with simple graph algorithms, over graph analytics, up to Graph Neural Networks.
In particular, the workshop will cover the following topics:
– Why graphs are such a powerful abstraction
– What use-cases are suitable for Graph-based Machine Learning
– How to leverage knowledge graphs
– The graph ecosystem including its many many powerful open source tools
– How to extract value from graphs using graph analytics and graph algorithms
– How to combine deep learning and graphs
– How we can learn graphs features using graph neural networks

'Graph & Beyond' Lunch Break
#7 – Getting Started with ArangoDB Oasis
April 7th, 2021 – 12pm
Looking to get a glimpse of ArangoDB Oasis, the managed cloud service for ArangoDB?
In this Lunch Break, Senior Software Developer Robert Stam will introduce the Oasis platform.

'Graph & Beyond' Lunch Break
#6 – AQL Query Performance Optimization 101 (Part II)
March 24th, 2021 – 12pm
This is Part 2 of the Lunch Break sessions covering the basics of AQL query performance optimization. This Lunch Break session covers AQL query performance optimizations for cluster setups. It is a follow-up session to the AQL query performance optimization session from 2 weeks earlier.
Codemotion Conference 2021

Jörg Schad “RDF vs. Property Graphs: Friends or Foes?”
When it comes to (knowledge) graphs, the world is divided between RDF and Property Graph. And even in 2021 these camps appear disjoint with some preferring the standardized semantics of RDF while others prefer the ease-of-use and flexibility of property graphs. In this talk we will first discuss the individual strengths and weaknesses of each system and then look at different use-cases (in particular knowledge graphs). Furthermore, we will see that in many cases they are actually best used together, combining their strengths.
O'Reilly Live Training

Jörg Schad “Graph Powered Machine Learning First Steps”
Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.
Join expert Jörg Schad to explore the symbiosis of graphs and machine learning, starting with graph analytics to graph neural networks. You’ll learn why graphs are such a powerful abstraction and discover how to leverage them in your machine learning projects.

'Graph & Beyond' Lunch Break
#5 – AQL Query Performance Optimization 101 (Part I)
March 10th, 2021 – 12pm
This is Part 1 of 2 Lunch Break sessions covering the basics of AQL query performance optimization. Jan Steemann will check if and how indexes can help AQL query performance, and how to set up the indexes so that they help most.

'Graph & Beyond' Lunch Break
#4 – Graph Analytics with ArangoDB
February 24th, 2021 – 12pm
In this Graph & Beyond Lunch Session you learn about Graph Analytics with ArangoDB. We will take a short look at different graph algorithms such as Community Detection, Centrality Measures, and Recommendation, but also discuss challenges of scaling such analytics to enterprise use cases.

'Graph & Beyond' Lunch Break
#3 – AQL for eCommerce Analytics
February 17th, 2021 – 12pm
During this Lunch Session, Jackson Reimers will showcase various AQL queries to analyze an eCommerce dataset for common questions in retail, such as:
- What products are offered?
- Where are they located in my store?
- Which are the best selling products?
- What can the data tell me about shopper purchasing behaviors?

'Graph & Beyond' Lunch Break
#2 – ArangoSearch – Where Full-Text Search and Graphs Meet
February 10th, 2021 – 12pm
In this Graph and Beyond Lunch session we will cover ArangoSearch, the full-text search engine natively integrated into ArangoDB. Chris Woodward will give an overview of how you can combine a wide range of search queries (e.g., phrase, proximity, or range), ranking (e.g., BM25 or TFIDF algorithms) and ArangoDB’s other data models (e.g., graph or document).
Webinar

Graph Analytics with ArangoDB

Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization and other NLP tasks. In this hands-on workshop, we will explore a number of use cases suitable for Graph Analytics — and in particular leverage ArangoDB’s Graph Algorithms Library.

'Graph & Beyond' Lunch Break
#1 – Fraud Detection with ArangoDB
February 3rd, 2021 – 12pm
Learn how to use the multi-model graph capabilities within ArangoDB to identify fraud patterns (as used for money laundering) in financial datasets and catch some bad guys! Jackson Reimers will show various fraud patterns and how to detect them, using the ArangoDB graph visualizer.
Webinar

ArangoDB Webinar: Getting Started with ArangoDB Oasis

Looking to move to the cloud this year? Curious to get a glimpse of Oasis, the easiest way to run ArangoDB?
Join Ewout Prangsma, ArangoDB Oasis Architect and Team Lead, for a walk-through on how to get started, as well as get the most out of the platform. Topics covered will include:
- Creating your first deployment
- How to choose the right configuration and node size
- Inviting colleagues to join you
- Security best practices
- Introduction to automation with Oasis
- Audience Q&A
O'Reilly Live Training

Jörg Schad “Graph Powered Machine Learning First Steps”
Many powerful machine learning algorithms—including PageRank (Pregel), recommendation engines (collaborative filtering), and text summarization and other NLP tasks—are based on graphs. And there are even more applications once you consider data preprocessing and feature engineering, which are both vital tasks in machine learning pipelines.
Join expert Jörg Schad to explore the symbiosis of graphs and machine learning, starting with graph analytics to graph neural networks. You’ll learn why graphs are such a powerful abstraction and discover how to leverage them in your machine learning projects.
Knowledge Connexions

Jörg Schad “Graph Powered Machine Learning”
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks.
There are even more applications once we consider data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines.
In this workshop, we will overview the intersection of graphs and Machine Learning from Graph Analytics to Graph Neural Networks.
Join Joerg Schad at 10 AM PT.
Data Science UA Conference

Jörg Schad “Building OpenSource Machine Learning Pipelines”
This workshop gives an overview into building a complete automated deep learning pipeline starting with exploratory analysis, overtraining, model storage, model serving, and monitoring and answer questions such as:
- How can we enable data scientists to exploratively develop models?
- How to automatize distributed Training, Model Optimization, and serving using CI/CD?
- How can we easily deploy these distributed deep learning frameworks on any public or private infrastructure?
- How can we manage multiple different deep learning frameworks on a single cluster, especially considering heterogeneous resources such as GPU?
- How can we store and serve models at scale?
- What Metadata should be stored in a production setup?
- How can we monitor the entire pipeline and track the performance of the deployed models?
The participants will build an end-to-end data analytics pipeline including:
- Pipeline Orchestration with TFX, Kubeflow, and Airflow
- Data preparation
- Jupyter Notebooks
- Distributed training with TensorFlow
- Automation & CI/CD using Jenkins and Argo
- Model and metadata storage
- Model serving and monitoring
Webinar

ArangoDB Feature Preview: Custom Pregel

Pregel, originally developed by Google, is a framework for iterative, distributed Graph Processing. It has been supported by ArangoDB for almost four years in the form of C++ algorithms. With the upcoming version 3.8 of ArangoDB, we will introduce a more flexible version of Pregel, which will allow users to specify custom Pregel algorithms for a running ArangoDB cluster without having to write C++ code.
In this webinar Heiko Kernbach, software developer at ArangoDB, and Jörg Schad, Head of Engineering, will provide an overview of the new custom Pregel support (which can already be tested using the latest nightly builds). We will have plenty of time at the end for an open discussion about a number of tradeoffs, interface options, and use cases.
All Things Open 2020

Jörg Schad and Chris Woodward: “Challenges in Building Multi-Cloud Provider Platform with Managed Kubernetes”
Building a cloud-agnostic platform used to be a challenging task as one had to deal with a large number of different cloud APIs and service offerings. Today, as most Cloud providers are offering a managed Kubernetes solution (e.g., GKE, AKS, or EKS), it seems like developers could simply build a platform based on Kubernetes and be cloud-agnostic. While this assumption is mostly correct, there are still a number of differences and pitfalls when deploying across those managed Kubernetes solutions.

Chris Woodward: “Hacktoberfest – Intro to Knowledge Graphs”

Have you ever wondered how Google is able to provide those little information cards when you do a search?
How about how Wikipedia is able to catalog the world’s information and make it useful to humans?
Well, in short, the answer to these questions and many other use cases is that they use Knowledge Graphs.
There are various technologies involved with Knowledge Graphs and getting started with them can seem like a daunting task. However, this meetup is the first in a series that hopes to make Knowledge Graphs more approachable and to show how to utilize them with ArangoDB.
Webinar

Save Time & Do More “Cool Stuff” With Managed Database Service.


If you’re interested in seeing how a managed database can help bring a little Oasis to your life, please join us.
October 14th 2020, 10 AM PDT/ 1 PM EDT/ 7 PM CEST.
DevOpsCon Berlin

Jörg Schad: “Challenges in Building a Multi-Cloud-Provider Platform With Kubernetes”
Building a cloud-agnostic platform used to be a challenging task as one had to deal with a large number of different cloud APIs and service offerings. Today, as most Cloud providers are offering a managed Kubernetes solution (e.g., GKE, AKS, or EKS), it seems like developers could simply build a platform based on Kubernetes and be cloud-agnostic. While this assumption is mostly correct, there are still a number of differences and pitfalls when deploying across those managed Kubernetes solutions.
Developer Week Global: Cloud 2020

Jörg Schad and Chris Woodward: “Challenges in Building Multi-Cloud Provider Platform with Managed Kubernetes “
This talk discusses the experiences made while building the ArangoDB Managed Service offering across and GKE, AKS, or EKS.
Join Joerg and Chris September 29th at 8am PDT.
Webinar

ArangoDB 3.7 – Graph Performance at Scale

We are excited to share the features and improvements of the latest Arango 3.7 release!
If you want to learn about the latest updates for Graph Performance, Cluster Scalability, Kubernetes Operator improvements, ArangoSearch supporting Fuzzy search, Security, Schema validation, and many more, join Jörg Schad, our Head of Engineering and Machine Learning, in this webinar.
We are looking forward to celebrating the 3.7 release with you and receiving your feedback to make the upcoming releases even better!
Stackconf2020: The Open Source Infrastructure Conference

Jörg Schad: “Challenges in Building a Multi-Cloud-Provider Platform With Kubernetes”
Building a cloud-agnostic platform used to be a challenging task as one had to deal with a large number of different cloud APIs and service offerings. Today, as most Cloud providers are offering a managed Kubernetes solution (e.g., GKE, AKS, or EKS), it seems like developers could simply build a platform based on Kubernetes and be cloud-agnostic. While this assumption is mostly correct, there are still a number of differences and pitfalls when deploying across those managed Kubernetes solutions.
MLOPS: Production and Engineering World 2020

Jörg Schad: “Building an OSS Data Science Pipeline”
Join Joerg Schad for his online workshop “Building an OSS Data Science Pipeline.”
June 16th 2020.
Online Meetup

Community Pioneer: “How and Why I Built my CMS Based on ArangoDB and openresty”


Join us and our Community Pioneer, Olivier Bonnaure to learn more about his ArangoDB and openresty project.
May 20th, 2020 – 9AM PST/ 12PM EST/ 6PM CEST
Online Meetup

Community Pioneer: “Monitoring Political and Violent Risks Associated with the COVID-19 Crisis with reKnowledge.”


Join us and our Community Pioneer, Julien Grossmann – CEO of reKnowledge, for the next exciting talk on a currently very important topic.
April 28, 2020 – 9AM PST/ 12PM EST/ 6PM CEST
Meetup

JavaScript Armenia Meetup: “Seperate Your Data Access Layer With Typescript Powered Microservices”


We would like annouce our Community Pioneer Gurgen Hayrapetyan as he explains how he built his own project on ArangoDB.
Saturday, April 18, 2020 from 12:00 PM to 5:00 PM (Armenia Standard Time)
ODSC East 2020

Jörg Schad, Tutorial: “Graph Powered Machine Learning”
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization and other NLP tasks. There are even more applications once we consider data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines. In this course, we will investigate the intersection of graphs and Machine Learning.
Online Meetup

Community Pioneer: “Machine Learning + Immunology, how ArangoDB Saved My Life”


Join us for the next exciting online talk from our Community Pioneer, Tariq Daouda. During this meetup, Tariq will talk about how he used ArangoDB for managing data set and experiments and for ensuring reproducible results.
April 15, 2020 – 11AM PST/ 2PM EST/ 8PM CEST
Operatorcon

Online Session: “Challenges in Building a Multi-Cloud-Provider Platform With Managed Kubernetes”


This talk discusses the experiences made while building the ArangoDB Managed Service offering across and GKE, AKS, or EKS. While the (managed) Kubernetes API being a great abstraction from the actual cloud provider, a number of challenges remain including for example networking, autoscaler, cluster provisioning, or node sizing. This talk provides an overview of those challenges and also discuss how they were solved as part the ArangoDB managed Service.
Subscribe to the Webinar and you will receive confirmation per email.
Online Meetup

Community Pioneer: “RecallGraph – A versioning data store for time -variant graphs built on ArangoDB”


We would like welcome you to join a hands-on talk from our Community member – Aditya Mukhopadhyay as he explores RecallGraph – a Foxx Microservice for ArangoDB.
March 19th 2020 – 8:30 AM EDT / 1:30 PM CET / 6 PM IST
Webinar

gVisor, Kata Containers, Firecracker, Docker: Who is Who in the Container Space?

In this talk, we will provide an overview of the different containerization technologies, discuss their tradeoffs, and provide guidance for different use cases.
* We will define the term container in more detailed during the talk
** and yes we will also cover some of the pre-docker container space!
Join the Head of Engineering and Machine Learning, Jörg Schad in this webinar.
Online Meetup

Community Pioneer Online Meetup: “Separate Your Data Access Layer With TypeScript Powered MicroServices”


We would like to welcome you to the hands-on talk from our Community member – Gurgen Hayrapetyan, who will speak live about his project – ArangoDB Foxx Service Template with TypeScript & Yarn 2.0 Support.
March 4th 2020 – 9AM PT/ 12PM ET/ 6PM CET
Production-grade ML pipelines Meetup

Jörg Schad ” Production-grade ML Pipelines – From Data To Metadata”


In this talk, we look at existing implementations, in particular, MLMD as part of the TensorFlow ecosystem. Further, we propose the first draft of an (MLMD compatible) universal Metadata API. We demo the first implementation of this API using ArangoDB.
Workshop: Building and Operating an Open Source Data Science Platform

Jörg Schad “Production – grade ML Pipeline”


This workshop gives an overview into building a complete automated deep learning pipeline starting with exploratory analysis, overtraining, model storage, model serving, and monitoring and answer questions.
Dutch Kubernetes/Cloud-Native Meetup

Jörg Schad & Ewout Prangsma: “Challenges in Building Multi-Cloud-Provider Platform With Managed Kubernetes”


This talk discusses the experiences made while building the ArangoDB Managed Service offering across and GKE, AKS, or EKS. While the (managed) Kubernetes API being a great abstraction from the actual cloud provider, a number of challenges remain including for example networking, autoscaler, cluster provisioning, or node sizing. This talk provides an overview of those challenges and also discuss how they were solved as part the ArangoDB managed Service.
Webinar

ArangoML Pipeline Cloud – Manage Machine Learning Metadata

In this webinar, we will show how to leverage ArangoML Pipeline Cloud with your Machine Learning Pipeline by using an example notebook from the TensorFlow tutorial.
Join our Head of Engineering and Machine Learning, Jörg Schad and our Developer Engineer Chris Woodward, in this release webinar to learn more about the ArangoML Pipeline Cloud and how it can benefit your applications.
Webinar

ArangoDB 3.7 Roadmap: Performance at Scale

After the release of ArangoDB 3.6 we are starting to work on the next version with even more exciting features. As an open-source project we would love to hear your ideas and discuss the roadmap with our community.
Would you like to learn more about Satellite Graphs, Schema Validation, a number of performance and security improvements?
Than join Jörg Schad, Head of Engineering and Machine Learning at ArangoDB, who will share the latest plans for the upcoming ArangoDB 3.7 release as well as the long term roadmap.
Global Graph Summit/Data Day Texas

ArangoDB @ Global Graph Summit 2020


Look forward to meeting us at the Global Graph Summit 2020 at the ArangoDB booth and more! Details to follow soon.
Webinar

What’s new in ArangoDB 3.6?

Version 3.6 comes with a new OneShard deployment which is designed for use cases where you don’t need horizontal scalability, but still rely on high availability, fault tolerance and query performance which is similar to a single server. Maybe something for your next Graph use case or multi-tenant application? In this webinar, Ingo Friepoertner, the Product Manager at ArangoDB, will go a into the details on OneShard feature and other several performance improvements introduced with the 3.6 release.