ArangoML Series: Intro to NetworkX Adapter

00ArangoML, General, Graphs, how to, Machine LearningTags: , , , , ,

This post is the fifth in a series of posts introducing the ArangoML features and tools. This post introduces the NetworkX adapter, which makes it easy to analyze your graphs stored in ArangoDB with NetworkX.

In this post we:

  • Briefly introduce NetworkX
  • Explore the IMDB user rating dataset
  • Showcase the ArangoDB integration of NetworkX
  • Explore the centrality measures of the data using NetworkX
  • Store the experiment with arangopipe

This notebook is just a slice of the full-sized notebook available in the ArangoDB NetworkX adapter repository. It is summarized here to better fit the blog post format and provide a quick introduction to using the NetworkX adapter. 

ArangoML Pipeline Cloud graphic showing an example machine learning pipeline
More info

ArangoML Part 4: Detecting Covariate Shift in Datasets

00ArangoML, General, Graphs, Machine LearningTags: ,

This post is the fourth in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. Until now, we have focused on ArangoML’s ability to capture metadata for your machine learning projects, but it does much more. 

In this post we:

  • Introduce the concept of covariate shift in datasets
  • Showcase the built-in dataset shift detection API
ArangoML Pipeline Complete pipeline - ArangoDB Machine Learning
More info

ArangoML Pipeline Cloud – Managed Machine Learning Metadata Service

00GeneralTags: ,

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.

Read more

ArangoML Pipeline – A Common Metadata Layer for Machine Learning Pipelines

00GeneralTags: ,

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.

Read more

Do you like ArangoDB?
icon-githubStar this project on GitHub.
close-link