This document discusses machine learning and the Elastic Stack. It begins with some forward-looking statements about future offerings, expected performance, and uncertainties. It then discusses how machine learning can be used for anomaly detection in time series data to identify patterns and unusual behavior. Different types of time series anomaly detection models are described, including single metric, multi-metric, and population analysis models. The presentation concludes with a brief demo of machine learning in the Elastic Stack.

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Machine Learning and the
Elastic Stack: Everywhere
You Need It
Thomas Grabowski
Product Manager, Machine Learning

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This presentation and the accompanying oral presentation contain forward-looking statements, including statements
concerning plans for future offerings; the expected strength, performance or benefits of our offerings; and our future
operations and expected performance. These forward-looking statements are subject to the safe harbor provisions
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or products, except as required by law.
Forward-Looking Statements

3. Are there any important incidents?

4. Are these important incidents?
Are these spikes typical?
Does it seem like we are
getting more error msgs?

5. Why is there a spike in traffic?

6. Why is there a spike in traffic?
Are these spikes typical?
Is this related?

7. Drilling down surfaces more questions
Are these related?

8. Should this process be running?

9. Elastic Machine Learning
Anomaly Detection to
identify patterns and
unusual behavior in
historical and streaming
time series data

10. Let's build a model
What kinds of patterns can we find in time series data?

11. Types of Time Series Anomaly Detection models
Single metric Multi metric
High • Low • Count
Sum • Min • Max • Mean • Median

12. Types of Time Series Anomaly Detection models
Any unusual users? Any rare processes?
Population Analysis

13. Types of Time Series Anomaly Detection models
Categorization - Clustering of tokenized text

14. PredictLearn Operationalize

15. 15
Demo

16. Machine Learning in
The Elastic Stack
Machine Learning is reliably and
securely implemented across the
Stack

17. Thank You
Search. Observe. Protect.
Thank You!