The goal of this talk is to give a brief overview of the emerging field of Deep Learning and its increasing relevance to the Open Source community. I will use examples – mainly based on performance data – on the one hand to underline the difference between traditional Machine Learning and Deep Learning, and on the other hand to underline the potential of the latter. The current state of Keras, Tensorflow and the respective possibilities to use them together for instance with the SciPy stack, scikit-learn, as well as InfluxDB, and Grafana is going to be illustrated.

1. 1
Susanne Greiner
DEEP LEARNING
a recent trend and its potential
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SFSCON 2017

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3. 3
What is Deep Learning?
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AI
AI
ML
DL
Artificial Intelligence [AI]
intelligent behaviour by machines, rather than the natural
intelligence (NI) of humans and other animals
Machine Learning [ML]
a field of computer science that gives computers the
ability to learn without being explicitly programmed.
Deep Learning [DL]
part of a broader family of machine learning methods
based on learning data representations,
as opposed to task-specific algorithms.

4. 4
A Recent Trend?
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Not really… But…
https://www.thestar.com/news/world/2015/04/17/how-a-toronto-professors-research-revolutionized-artificial-intelligence.html
Geoffrey Hinton
BACK PROPAGATION ALGORITHM
Alexey Grigorevich Ivakhnenko
THE FATHER OF DEEP LEARNING
the Group Method
of Data Handling (GMDH)
Deep learning
is exploding:
DL Experts
>= 0 in 2014
41,000 2017
Facebook
Apple
Netflix
Microsoft
Google
Baidu
1968
1986

5. 5
How does it work?
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http://fortune.com/ai-artificial-intelligence-deep-machine-learning/
The perceptron: Artificial Neural Network:
INPUT
OUTPUT
INPUTLAYER
OUTPUT LAYER
HIDDEN LAYERS

6. 6
How does it work?
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7. 7
How can we use Deep Learning?
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Video Analytics with Deep Learning
https://www.nvidia.it/content/dam/en-zz/it_it/Solutions/deep-learning/deep-learning-solutions/

8. 8
How can we use Deep Learning?
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Smart Cities
 predicting crime
 optimizing transportation
 analyzing expected ROI of a franchise location
 predicting smog
 alleviating traffic
 better city planning
 better political policies

9. 9
How can we use Deep Learning?
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Performance Monitoring
Data Collection ≠ Problem Solution
The right decision at each step is not trivial!

10. Anomaly Detection
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Metrics
MODEL
STANDARD
BEHAVIOUR
TRAIN
DATA
• historical data of same metric
• historical data of similar metric
• historical data of similar machine
WHAT DO WE EXPECT?
TEST
DATA
• The farer away from expectation
the higher the RISK
• RISK: different and rare
HOW FAR ARE WE FROM OUR EXPECTATION?
RISK
SCORE
10

11. Culprit Detection
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IOs
HIGH RISK
PERIODS
• historical data of same metric
• historical data of similar metric
• historical data of similar machine
• historical data of neighbours
• Which (set of) machine(s)
is most probably causing
the high risk
• Proactive analysis to
prevent congestion
AUTOMATED
ANALYSIS
• Check machines with
high risk first, there might be
no need to control the others
Proactive search for
potential future culprits
Faster Troubleshooting
11

12. 12
Troubleshooting
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13. 13
Combination of Performance Metrics & User Experience
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14. 14
Open Source & Deep Learning
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15. 15
Conclusion
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software is out there,
instructions are out there,
go and play with it!
Domain knowledge and experience help to combine
to improve performance.
Notable reduction
of operational
monitoring costs
Proactive
prevention of
outages
YOU?
data + advanced methods + open source tools

16. 16
GRAZIE PER
LA VOSTRA ATTENZIONE!
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