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We all know good training data is crucial for data scientists to build quality machine learning models. But when productionizing Machine Learning, Metadata is equally important. Consider for example:
- Provenance of model allowing for reproducible builds
- Context to comply with GDPR, CCPA requirements
- Identifying data shift in your production data
This is the reason we built ArangoML Pipeline, a flexible Metadata store which can be used with your existing ML Pipeline.
ArangoML Pipeline Cloud allows you to start using ArangoML Pipeline without having to even start a separate docker container.
In this webinar, we show how to leverage ArangoML Pipeline Cloud with your Machine Learning Pipeline by using an example notebook from the TensorFlow tutorial.
Jörg Schad
About the Presenter:
Jörg Schad is Head of Engineering and Machine Learning at ArangoDB. In a previous life, he has worked on or built machine learning pipelines in healthcare, distributed systems at Mesosphere, and in-memory databases. He received his Ph.D. for research around distributed databases and data analytics. He’s a frequent speaker at meetups, international conferences, and lecture halls.
Chris Woodward
About the Presenter:
Chris has over 10 years experience at all angles of technology including service, support, and development. He is also passionate about learning and right now he is focused on improving the learning experience for the ArangoDB community. Chris believes the future is native multi-model and wants to help tell the world.