Paper Info
Title
AutoAI: Automating the End-to-End AI Lifecycle with Humans-in-the-Loop
Abstract
Automated Artificial Intelligence and Machine Learning (AutoAI / AutoML) can now automate every step of the end-to-end AI Lifecycle, from data cleaning, to algorithm selection, and to model deployment and monitoring in the machine learning workflow. AutoAI technologies, initially aimed to save data scientists from the low level coding tasks, also has great potential to serve non-technical users such as domain experts and business users to build and deploy machine learning models. Researchers coined it as "democratizing AI", where non-technical users are empowered by AutoAI technologies to create and adopt AI models. To realize such promise, AutoAI needs to translate and incorporate the real-world business logic and requirements into the automation. In this Demo, we present a first of its kinds experimental system, IBM AutoAI Playground, that enables non-technical users to define and customize their business goals (e.g., Prediction Time) as constraints. AutoAI then builds models to satisfy those constraints while optimizing for the model performance (e.g., ROC AUC score). This Demo also showcases AutoAIViz, a Conditional Parallel Coordinates visualization feature, and a TrustedAI feature from two accepted IUI'20 papers.
Year
DOI
Venue
2020
10.1145/3379336.3381474
IUI '20: 25th International Conference on Intelligent User Interfaces Cagliari Italy March, 2020
Keywords
DocType
ISBN
AutoAI, AutoML, parallel coordinates, human-AI collaboration, democratizing AI, business constraints, stakeholder constraints
Conference
978-1-4503-7513-9
Citations 
PageRank 
References 
2
0.38
0
Authors
11
Name
Order
Citations
PageRank
Dakuo Wang17314.74
Parikshit Ram2103.29
Daniel Karl I. Weidele341.42
Sijia Liu418142.37
Michael J. Muller52310303.58
Justin D. Weisz611119.46
Abel N. Valente791.28
Arunima Chaudhary821.05
Dustin Torres920.71
Horst Samulowitz1031626.05
Lisa Amini1135628.14