Features, Explainability, and Analytics OpML '20 Session 3

Join us for the OpML '20 session on Features, Explainability, and Analytics, hosted on the USENIX OpML Slack Workspace channel for our Ask-Me-Anything session with the authors. It will be Thursday, July 30 from 9am - 10:30am, PDT. To join, just join the free slack workspace above and go to the channel!

As production ML is used in more industries, businesses need to understand how the ML pipelines intersect with customer concerns such as data management, trust, and privacy. At the technical level, how features are built, evaluated, and managed is critical, as is the ability to monitor and explain ML in production.

In this session, four presentations cover topics of ML explainability, reproducibility, and feature management in production. Learn what it means to have explainable models in production, how to track, manage, and reproduce pipelines, and how to evaluate new ML pipelines!

Detecting Feature Eligibility Illusions in Enterprise AI Autopilots

Fabio Casati, Veeru Metha, Gopal Sarda, Sagar Davasam, and Kannan Govindarajan, Servicenow

SaaS Enterprise workflow companies, such as Salesforce and Servicenow, facilitate AI adoption by making it easy for customers to train AI models on top of workflow data, once they know the problem they want to solve and how to formulate it. However, as we experience over and over, it is very hard for customers to have this kind of knowledge for their processes, as it requires an awareness of the business and operational side of the process as well as of what AI could do on each with the specific data. The challenge we address is how to take customers to that stage, and in this paper we focus on a specific aspect of such challenge: the identification of which "useful inferences" AI could make and which process attributes can be leveraged as predictors, based on the data available for that customer.

Time Travel and Provenance for Machine Learning Pipelines

Alexandru A. Ormenisan, KTH - Royal Institute of Technology; Moritz Meister, Fabio Buso, and Robin Andersson, Logical Clocks AB; Seif Haridi and Jim Dowling, KTH - Royal Institute of Technology

Machine learning pipelines have become the defacto paradigm for productionizing machine learning applications as they clearly abstract the processing steps involved in transforming raw data into engineered features that are then used to train models. In this paper, we use a bottom-up method for capturing provenance information regarding the processing steps and artifacts produced in ML pipelines. Our approach is based on replacing traditional intrusive hooks in application code (to capture ML pipeline events) with standardized change-data-capture support in the systems involved in ML pipelines: the distributed file system, feature store, resource manager, and applications themselves. In particular, we leverage data versioning and time-travel capabilities in our feature store to show how provenance can enable model reproducibility and debugging.

An Experimentation and Analytics Framework for Large-Scale AI Operations Platforms

Thomas Rausch, TU Wien; Waldemar Hummer and Vinod Muthusamy, IBM Research AI

This paper presents a trace-driven experimentation and analytics framework that allows researchers and engineers to devise and evaluate operational strategies for large-scale AI workflow systems. Analytics data from a production-grade AI platform developed at IBM are used to build a comprehensive system and simulation model. Synthetic traces are made available for ad-hoc exploration as well as statistical analysis of experiments to test and examine pipeline scheduling, cluster resource allocation, or similar operational mechanisms.

Challenges Towards Production-Ready Explainable Machine Learning

Lisa Veiber, Kevin Allix, Yusuf Arslan, Tegawendé F. Bissyandé, and Jacques Klein, SnT – Univ. of Luxembourg

Machine Learning (ML) is increasingly prominent in organizations. While those algorithms can provide near perfect accuracy, their decision-making process remains opaque. In a context of accelerating regulation in Artificial Intelligence (AI) and deepening user awareness, explainability has become a priority notably in critical healthcare and financial environments. The various frameworks developed often overlook their integration into operational applications as discovered with our industrial partner. In this paper, explainability in ML and its relevance to our industrial partner is presented. We then discuss the main challenges to the integration of explainability frameworks in production we have faced. Finally, we provide recommendations given those challenges.

Please join us for this fascinating discussion!

Joel Young and Nisha Talagala, USENIX OpML '20 Co-Chairs