|Date:||June 23, 2016|
3009 157th Pl Ne, Redmond, WA
ABOUT THE EVENT
This event is not to be missed! Ted Dunning, Chief Application Architect at MapR, will be giving a talk on anomaly detection. Ted is an amazing speaker. If there is one talk you don't want to miss this year, this would be it. (And we have some seriously good speakers and topics--that's how awesome Ted is!)
Registration is open to all DAML members. Due to limited space, we are disabling guests for this event. Friends are welcome, they'll just need to register for the group first.
This event will be hosted on the Microsoft main campus, Building 27. See map at the bottom of the page. Food is generously sponsored by MapR.
Arrive early for food. The talk will start around 6:30pm. There will be plenty of time for questions after.
The Seattle Data/Analysis/Machine Learning Meetup Group is a relatively informal group of Seattle-area data scientists, software engineers, and related people. They meet monthly at tech companies who are willing to host them in downtown Seattle and Bellevue, and have a format which varies from a series of lightning talks on different topics, to having visiting or local speakers give hour-long tutorials or tech talks.
- Ted Dunning - How to Find What You Didn't Know to Look For, Practical Anomaly Detection
Anomaly detection is the art of automating surprise. To do this, we have to be able to define what we mean by normal and recognize what it means to be different from that. The basic ideas of anomaly detection are simple. You build a model and you look for data points that don’t match that model. The mathematical underpinnings of this can be quite daunting, but modern approaches provide ways to solve the problem in many common situations.
We will describe these modern approaches with particular emphasis on several real use-cases including:
- rate shifts to determine when events such as web traffic, purchases or process progress beacons shift rate.
- time series generated by machines or biomedical measurements.
- topic spotting to determine when new topics appear in a content stream such as Twitter.
- network flow anomalies to determine when systems with defined inputs and outputs act strangely.
In building a practical anomaly detection system you have to deal with practical details starting with algorithm selection, data flow architecture, anomaly alerting, user interfaces and visualizations.
We will show how to deal with each of these aspects of the problem with an emphasis on realistic system design.
Ted Dunning is Chief Application Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects . Ted has been very active in mentoring new Apache projects and is currently serving as vice president of incubation for the Apache Software Foundation . Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems. He built fraud detection systems for ID Analytics (LifeLock) and he has 24 patents issued to date and a dozen pending. Ted has a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.
- Ted Dunning
- Slideck:Anomaly detection-2016
- Feedback 5 minute survey - Help us improve by filling the survey
- More Materials from Ted Dunning | MapR Blog
- Ebooks by Ted Dunning & Ellen Friedman:
- Streaming Architecture: New Designs Using Apache Kafka and MapR Streams | MapR ebook
- Sharing Big Data Safely: Managing data Security | MapR ebook
- Real-World Hadoop | MapR ebook
- Time Series Databases: New Ways to Store and Access Data: New Ways to Store and Access Data | MapR ebook
- Practical Machine Learning: A New Look at Anomaly Detection | MapR ebook
- Practical Machine Learning: Innovations in Recommendation | MapR ebook
OTHER RELATED MATERIALS
- Finding the Zebra in a Herd of Ponies- A new look at anomaly detection | MapR Blog
- Use Case: Machine Learning at American Express: Benefits and Requirements | MapR Blog
- Better Anomaly Detection with the T-Digest #WhiteboardWalkthrough - YouTube
- Parallel and Iterative Processing for Machine Learning Recommendations with Spark
- Machine Unlearning: The Value of Imperfect Models | MapR Blog
- MapR Quick Start Solution - Recommendation Engine Demo - YouTube
- Find more on: spark mahout machine learning
For exact directions and more details visit: http://www.meetup.com/Seattle-DAML/events/231426676/