Production machine learning systems require constant monitoring, not just to keep the system online but also to ensure the model inference results are correct. This is much more straightforward when user feedback or labels are available. In those cases, the model performance can be tracked and periodically reevaluated using standard metrics such as precision, recall, or AUC. But what about when labeled data is lacking? In many applications, labels are expensive to obtain (requiring human analysts’ manual review) or cannot be obtained in a timely manner (e.g., not available until weeks or months later).
Ting-Fang Yen discusses the design and implementation of a real-time system to monitor production machine learning systems. The approach is designed to discover detection anomalies, such as volume spikes caused by spurious false positives, as well as gradual concept drifts when the model is no longer able to capture the target concept. In either case, it is able to automatically detect undesirable model behaviors early.
Part of the approach borrows from signal processing techniques for time series decomposition, where the time series can be used to represent a sequence of model decisions on different types of input data, or the amount of deviation between consecutive model runs. The approach calculates cross-correlation among the identified anomalies to facilitate root cause analysis of the model behavior.
This work is a step toward automated deployment of machine learning in production as well as new tools for interpreting model inference results.
Ting-Fang Yen is the director of research at DataVisor, the leading fraud, crime, and abuse-detection solution using unsupervised machine learning to detect fraudulent and malicious activity such as fake account registrations, fraudulent transactions, spam, account takeovers, and more. She has over 10 years of experience in applying big data analytics and machine learning to tackle problems in cybersecurity. Ting-Fang holds a PhD in electrical and computer engineering from Carnegie Mellon University.
Comments on this page are now closed.
For exhibition and sponsorship opportunities, email email@example.com
For information on trade opportunities with O'Reilly conferences, email firstname.lastname@example.org
View a complete list of Strata Data Conference contacts
©2019, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • email@example.com
I saw that you are a speaker at the Strata Conference; I’m looking forward to your presentation. I have been following your company for the last couple of months and I am very impressed with your success. We have also spoke a few times on LinkedIn.
I would love to introduce myself and discuss a strategic partnership with your company. I have helped many companies in the Big Data and Analytics space such as Amazon, Bosch, Samsung, Graphcore, LG Eletronics MapR, Platfora, Couchbase, Snowflake, MongoDB, E8 Security with their challenging hiring needs.
I am looking forward to meeting you soon. Good luck with your speech.