Towards Automating the AI Operations Lifecycle
Matthew Arnold, Jeffrey Boston, et al.
MLSys 2020
Building models for natural language processing (NLP) tasks remains a daunting task for many, requiring significant technical expertise, efforts, and resources. In this demonstration, we present AutoText, an end-to-end AutoAI framework for text, to lower the barrier of entry in building NLP models. AutoText combines state-of-the-art AutoAI optimization techniques and learning algorithms for NLP tasks into a single extensible framework. Through its simple, yet powerful UI, non-AI experts (e.g., domain experts) can quickly generate performant NLP models with support to both control (e.g., via specifying constraints) and understand learned models.
Matthew Arnold, Jeffrey Boston, et al.
MLSys 2020
Ingkarat Rak-amnouykit, Ana Milanova, et al.
ICLR 2021
Shiqiang Wang, Nathalie Baracaldo Angel, et al.
NeurIPS 2022
Chih-kai Ting, Karl Munson, et al.
AAAI 2023