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, to feature engineering, 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 business logic and real-world 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 integrates a Conditional Parallel Coordinates visualization component for users to quickly examine dozens of models at a glance, and a TrustedAI feature, which are accepted as two IUI'20 papers.