AutoML 2.0: Control what you want, automate the rest

New low-code approaches to machine learning pioneered by Uber, Apple, and Meta 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, existing commercial AutoML solutions lack flexibility and support for unstructured data, and typically don’t perform well for complex deep-learning use cases. A new generation of AutoML technologies—like those pioneered at Uber, Apple, and Meta—aim to change that. These declarative machine learning systems provide a glass-box approach to automating ML that enables data teams to bring new models to market faster with complete flexibility and control, and the power to work with unstructured data sets for a broad range of use cases like NLP and Computer Vision. Join this webinar and demo to learn: ● Why current AutoML solutions fall short ● What are declarative ML systems with a deep dive on open-source Ludwig from Uber ● How to build state-of-the-art deep learning models in <15 lines of code with a config-driven approach ● How to rapidly train, iterate, and deploy an ML model on structured and unstructured data with Ludwig and Predibase