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.
