Estimated reading time: 4 minutes

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.

Estimated reading time: 4 minutes

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)