Chris Woodward

With the rapid and recent rise of data science, the Machine Learning Platforms being built are becoming more complex. For example consider the various Kubeflow components: Distributed Training, Jupyter Notebooks, CI/CD, Hyperparameter Optimization, Feature store, and more. Each of these components is producing metadata: Different (versions) Datasets, different versions of jupyter notebooks, different training parameters, test/training accuracy, different features, model serving statistics, and many more.
For production use, it is critical to have a common view across all these metadata as we have to ask questions such as: Which Jupyter notebook has been used to build Model xyz current running in production? If there is new data for a given dataset, which models (currently serving in production) have to be updated?

As the overall Machine Learning stack is still rapidly changing (and also different companies typically choose different components for their stack) with new tools coming out every month (if not week), it seems key to specify a generic API first supporting new and different components. Furthermore, Data Scientists need a simple model and intuitive interface to query across all metadata.

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. This talk provides an overview of those challenges and also discusses how they were solved as part of the ArangoDB managed Service.

Get introduced to the native multi-model database approach with hands on examples using the ArangoDB Query Language.

In this webinar, Chris & Jan will walk you through the basic concepts, key features and query options you have within ArangoDB as well as discuss scalability considerations for different data models. Chris is the hands-on guy and will showcase a variety of query options you have with a native multi-model database like ArangoDB, including:

• Fast, simple lookups
• Aggregations
• Join Operations
• Various Graphs Traversals
• And a first glimpse about what you can do with SmartGraphs

Chris & Jan hope to see you in this free webinar and are happy to answer all your questions in the Q&A part at the end

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.

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.

Some terms and concepts that this meetup will cover include:

– What is a Knowledge Graph?

– What is an ontology?

– RDF, OWL, TTL, SPARQL.. acronyms oh my!

– Interactive notebook on interpolating triples to a property graph.