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

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.

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.

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.

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!

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!

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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:

1. Why are most of the in-house developments so similar to Futures in terms of API?
2. What API should Futures have, and how can it be most efficiently implemented?
3. Many different optimizations: reducing the number of locks, false awakenings, allocations.

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. 

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.

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.

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.

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.

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

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.

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 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.

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

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.

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.

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

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.

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.

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.

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.

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.

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.

Jörg Schad: “Building an OSS Data Science Pipeline”

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

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

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)

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.

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

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.

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

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.

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

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.

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.

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.

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.

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.

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.

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.