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Expo Demonstration
Beyond AutoML: AI Automation & Scaling
Lisa Amini · Nitin Gupta · Parikshit Ram · Kiran Kate · Bhanukiran Vinzamuri · Nathalie Baracaldo · Martin Korytak · Daniel K Weidele · Dakuo Wang

Sun Dec 06 03:00 PM -- 04:00 PM (PST) @

There has been a recent burst in “AutoML” techniques as a means to automate the creation of ML models without necessary domain expertise. This demonstration looks well beyond AutoML’s current narrow focus on automated model building, to tackling automation across the full end-to-end AI/ML lifecycle. In industry settings, the AI/ML lifecycle typically includes a series of labor-intensive tasks such as preparing data, training models, deploying the selected model in cloud, monitoring performance, identifying faults, and taking corrective actions when failures or new business requirements occur. Enormous opportunities exist for scaling, automating, and accelerating this AI/ML lifecycle.

In this session, we demonstrate tools and research results in driving automation across the entire AI/ML lifecycle: from assessing data readiness and recommending mitigations, to semantically-driven automation based on concept discovery and knowledge augmentation, to advanced ML model building with business and fairness constraints, to novel pipelines for industry-critical modalities, to automation for monitoring models in deployment, recognizing deficiencies and recommending corrective actions. We will also demonstrate practical methods for scaling in multi-cloud environments with federated learning, and accelerated cloud-based inference of widely-popular classical ML algorithms such as XGBoost and LightGBM.

Author Information

Lisa Amini (IBM Research)
Nitin Gupta (IBM Research)
Parikshit Ram (IBM Research)
Kiran Kate (IBM Research)
Bhanukiran Vinzamuri (IBM Research)
Nathalie Baracaldo (IBM Research AI)

Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Her team focuses on two main areas: federated learning, where models are trained without directly accessing training data and adversarial machine learning, where defenses are designed to withstand potential attacks to the machine learning pipeline. Nathalie is the primary investigator for the DARPA program Guaranteeing AI Robustness Against Deception (GARD), where AI security is investigated. Her team contributes to the Adversarial Robustness 360 Toolbox (ART). Nathalie is also the co-editor of the book: “Federated Learning: A Comprehensive Overview of Methods and Applications”, 2022 available in paper and as e-book in Springer, Apple books and Amazon. Nathalie's primary research interests lie at the intersection of information security, privacy and trust. As part of her work, she has also designed and implemented secure systems in the areas of cloud computing, Platform as a Service, secure data sharing and Internet of the Things. She has also contributed to projects to design scalable systems that monitor, manage performance and manage service level agreements in cloud environments. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM Intellectual Property and innovation. Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI initiative. Nathalie is associated Editor IEEE Transactions on Service Computing. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016. Her dissertation focused on preventing insider threats through the use of adaptive access control systems that integrate multiple sources of contextual information. Some of the topics that she has explored in the past include secure storage systems, privacy in online social networks, secure interoperability in distributed systems, risk management and trust evaluation. During her Ph.D. studies she received the 2014 Allen Kent Award for Outstanding Contributions to the Graduate Program in Information Science by the School of Information Sciences at the University of Pittsburgh. Nathalie also holds a master’s degree with Cum Laude distinction in computer sciences from the Universidad de los Andes, Colombia. Prior to that, she earned two undergraduate degrees in Computer Science and Industrial Engineering at the same university.

Martin Korytak (IBM Research)
Daniel K Weidele (IBM Research)
Dakuo Wang (IBM)

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