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”

 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 “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 “Building and Operating an Open Source Data Science Platform”

June 11th, 2021 – 2-6pm (CET)

There are many great tutorials for training your deep learning models using 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, and monitoring and answer questions such as:

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.