Craig Shallahamer

President & Founder, OraPub Inc. Oracle Ace Director

OraPub, Inc. USA


Craig is a long time Oracle DBA who specializes in Oracle tuning and machine learning and started the OraPub website in 1995. Craig is a performance researcher and blogger, consultant, author of two books, an enthusiastic conference speaker and a passionate teacher to thousands of Oracle professionals. He clearly pushes the teaching envelope with his performance-focused membership program, webinars and videos! Craig has received a number of technical, effectiveness and community involvement awards. Craig is also an Oracle ACE Director.


How To Detect Anomalous Performance Using Machine Learning And Python

Event: 2020 Webinar Series

Stream:Emerging Technologies

Suppose you're doing an AWR or ASH based performance analysis and said, "I've NEVER seen this before! We'd better check it out." What you just identified was an anomalous performance event that warrants your attention. With the help of machine learning and the usual AWR and ASH data, we can confidently detect an anomalous performance event and alert an Oracle performance specialist in under a second! How to do this step-by-step, is what this presentation is all about. Join me as I build a "Performance Anomaly Detector" using 100% free industry standard tools, such as Python with machine learning libraries and the "Always Free" Oracle Autonomous Database with a Jupyter notebook. All presentation slides, scripts, notebook and a recording of the demonstration is available!

How Use Machine Learning, When You're Not Supposed To

Event: 2020 Webinar Series

Stream:Emerging Technologies

Most Oracle performance alerting is based on simple rules. For sure, some statistics are used, but still it's based on simple rules. With machine learning (ML) we easily go beyond simple rules, because ML algorithms are built to recognize patterns in data. The pattern recognition ability of ML goes far beyond what even a highly trained performance expert could ever hope to achieve. However, most monitoring and alerting systems do not truly use ML, many IT organizations are not ready to embrace ML and many DBAs are not cross-trained in ML. A novel solution to this problem is to use ML to create a list of rules that define "poor performance" based on your real system and real users. These rules may not even make sense to us, but they will take your alerting to a higher level because they are based on your data and ML algorithms. Join me for a fascinating presentation about how to use the results of ML as inputs into your existing monitoring and alerting systems.