Presentation: Understanding Software System Behavior With ML and Time Series Data

Track: Handling Sequential Data Like an Expert / ML Applied to Operations

Location: Cyril Magnin II

Duration: 9:20am - 10:10am

Day of week: Wednesday

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Abstract

Powered by the rise of cloud technology and ubiquitous mobile connectivity, software systems have utterly transformed daily life and the global economy. However, the reliable operation of these systems has been made increasingly difficult by their sheer scale, complexity, and rapid pace of evolution.
In this talk we discuss how time series datasets collected from running software can be combined with machine learning techniques in order to aid in the understanding of system behaviors in order to improve performance and uptime.

Speaker: David Andrzejewski

Engineering Manager @SumoLogic

David Andrzejewski is an Engineering Manager at Sumo Logic and co-organizer of the SF Bay Area Machine Learning meetup group. Prior to Sumo Logic, David held a postdoctoral research position working on knowledge discovery at Lawrence Livermore National Laboratory (LLNL). He completed his PhD in Computer Sciences at the University of Wisconsin-Madison in 2010, where he had also previously received an M.S. in Computer Sciences and a B.S. in Computer Engineering, Mathematics and Computer Sciences.

Find David Andrzejewski at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.