Joerg Schad - ArangoDB

Take Alpha 2 of the upcoming ArangoDB 3.7 for a spin!

March 26, 202000GeneralTags: Community, Enterprise, Release

We are 11 weeks into the development of ArangoDB 3.7 and want to give you yet another opportunity to try out the upcoming features before the release. On our technical preview page, you’ll find the Alpha 2 packages for the Community and Enterprise Edition.

This Alpha 2 comes with pretty neat features and improvements and we hope to get your early feedback!

This is particularly helpful for us to adjust our development in terms of solving real-world problems for you and ease-of-use for the new capabilities like: Read more

Upcoming ArangoDB 3.7 and Storage Engines

March 18, 202000GeneralTags: RocksDB, storage

TL;DR

ArangoDB has supported two storage engines for a while: RocksDB and MMFiles. While ArangoDB started out with just the MMFiles storage engine in its early days, RocksDB became the default storage engine in the 3.4 release. Due to its drawbacks ArangoDB 3.6 deprecated the old MMFiles storage engine and with the upcoming 3.7 release we plan to fully remove support. This blog post will provide the background of why storage engines matter, why we chose to deprecate the MMFiles storage engine, and what you should be aware of when migrating from MMFiles to the RocksDB storage engine. Read more

Celebrating Kube-ArangoDB’s 1.0 Release!

March 3, 202000GeneralTags: Kubernetes

Kube-ArangoDB, ArangoDB’s Kubernetes Operator first released two years ago and as of today is operating many ArangoDB production clusters (including ArangoDB’s Managed Service Oasis). With many exciting features we felt kube-arango really deserves to be released as 1.0.

ArangoML Pipeline Cloud – Managed Machine Learning Metadata Service

January 30, 202000GeneralTags: arangoml, Machine Learning

We all know how crucial training data for data scientists is to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important.

Consider for example:

Capture of Lineage Information (e.g., Which dataset influences which Model?)
Capture of Audit Information (e.g, A given model was trained two months ago with the following training/validation performance)
Reproducible Model Training
Model Serving Policy (e.g., Which model should be deployed in production based on training statistics)

If you would like to see a live demo of ArangoML Pipeline Cloud, join our Head of Engineering and Machine Learning, Jörg Schad, on February 13, 2020 – 10am PT/ 1pm ET/ 7pm CET for a live webinar.

ArangoML Pipeline – A Common Metadata Layer for Machine Learning Pipelines

September 25, 201900GeneralTags: arangoml, ML

Over the past two years, many of our customers have productionized their machine learning pipelines. Most pipeline components create some kind of metadata which is important to learn from.

This metadata is often unstructured (e.g. Tensorflow’s training metadata is different from PyTorch), which fits nicely into the flexibility of JSON, but what creates the highest value for DataOps & Data Scientists is when connections between this metadata is brought into context using graph technology…. so, we had this idea… and made the result open-source.

We are excited to share ArangoML Pipeline with everybody today – A common and extensible metadata layer for ML pipelines which allows Data Scientists and DataOps to manage all information related to their ML pipelines in one place.