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

• Introduce bootstrapping and bias-variance concepts
• Estimate and analyze the variance of the model from part 2
• Capture the metadata for this activity with arangopipe

This post is the second in a series of posts about machine learning and showcasing the benefits ArangoML adds to your machine learning pipelines. In this post we:

• Introduce machine learning concepts
• Demonstrate basic model building
• Log a model building activity with arangopipe

This post is the first in a series of posts introducing ArangoML and showcasing its benefits to your machine learning pipelines. In this first post, we look at what exactly ArangoML is, with later posts in this series showcasing the different tools and use cases.