Скачать или смотреть Intro to Declarative Machine Learning Systems: The Future of Low-code ML

Building ML solutions from scratch is a challenge: complex low-level code and long dev cycles make it hard to deploy a single model in less than 6 mos. On the other hand, AutoML solutions lack flexibility and support for unstructured data, and typically don’t perform well for complex deep learning use cases.

Fortunately, declarative machine learning systems—like those started at Uber and Apple—provide a glass-box approach to automating ML that enables data teams to bring new models to market faster with flexibility and control.

Watch this webinar and demo to learn:

About declarative ML systems incl. open-source Ludwig from Uber
How to build state-of-the-art models in less than 15 lines of code with a config-driven approach
How to rapidly train, iterate, and deploy a multimodal deep learning model with Predibase and Ludwig

Learn more: www.predibase.com