When: Sep 14 2021, 15:20 - 16:20 CEST

Abstract: Rarely are ML-based systems in the real world about training a model over some centralised datasets and then integrating the model into a deployed system. The architecture complexity of modern ML-based systems is multifaceted. Data and compute often have to be federated or at the edge due to data ownership, security, privacy, regulatory and ethical constraints. Data, models, code and configurations give rise to intermingled dependencies, not just within a single organisation but in an ecosystem. Model-as-a-service (including "foundation models" and their adaptation) and "continuous everything" introduces new uncertainties. This talk will outline the opportunities in software architecture to help address these challenges via distributed trust, federated ML-based systems, design patterns and other architecture-level assurance mechanisms.

Bio: Dr. Liming Zhu is a Research Director at Data61, the digital/data innovation arm of Australia’s national science agency, CSIRO. He is a conjoint full professor at University of New South Wales (UNSW). He serves as the chairperson of Standards Australia's blockchain committee and is on various AI trustworthiness-related ISO committees. His research program innovates in the areas of AI/ML platforms, responsible/ethical AI, software engineering, blockchain, regulation technology, quantum software, privacy and cybersecurity. He has published more than 200 academic papers on software architecture, secure and dependable systems, data/ML infrastructure, continuous deployment/DevOps and blockchain. His current research interests include distributed trust, software engineering for AI and responsible AI.