Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2A6lx09. Dave Casper talks about how modern data science and algorithms are being applied to "fight machines with machines". Filmed at qconsf.com. Dave Casper is CTO at Moogsoft. Previously, he served as Senior Vice President and Executive Director at several of the world's largest financial institutions, including UBS, Merrill Lynch, Bank of America, and Deutsche Bank and also co-chaired the Open Data Center Alliance Infrastructure Group.

1. Monitoring Modern Architectures
with Data Science
QCon 2017
Dave Casper, CTO

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Watch the video with slide
synchronization on InfoQ.com!
https://www.infoq.com/presentations/
modern-architectures-data-science

3. Purpose of QCon
- to empower software development by facilitating the spread of
knowledge and innovation
Strategy
- practitioner-driven conference designed for YOU: influencers of
change and innovation in your teams
- speakers and topics driving the evolution and innovation
- connecting and catalyzing the influencers and innovators
Highlights
- attended by more than 12,000 delegates since 2007
- held in 9 cities worldwide
Presented at QCon San Francisco
www.qconsf.com

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Much has changed since simple distributed client/server
architectures and so-too have the technologies and industry
practices around monitoring.
Cloud-Native, DevOps, blue/green deployments,
server-less, edge/fog, IoT all fit into a world much better
handled by the emerging Artificial Intelligence for IT
Operations domain more-so than traditional ITIL/SDLC
approaches.
Abstract

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Software continues to eat the world. Software automates,
defines.
The world is "going digital" and it's quite exciting -- but this
always-connected from-everything-to-everywhere world
adds complexity to software systems and this talk will dive
in to some of that complexity and how modern data science
and algorithms are being applied to "fight machines with
machines," so to speak.
Abstract

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-25822282
623992118
1343963318

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moogsoft

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discovery
monitoring
(observing)
analytics

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fluid
infrastructure
containers dc/os server-less software defined/dynamic

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anything
anywhere
anytime
data/txfrom
mobile IoT bots/RUM

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"if/else"
rules
algorithms
ML
millions millions
noise filt.
clustering prc
deja
vu

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AIOps
AI for IT Ops

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customer/
business
perspective

17. COURAGE INSIGHT
CONTEXT VELOCITY
ARE YOU READY
TO GO DIGITAL ?
This slide courtesy Andy Brown, Sandhill East
https://www.linkedin.com/in/andybrown63/

18. “Silicon Valley is coming.
There are hundreds of startups with a lot
of brains and money working on various
alternatives to traditional banking. They
are very good at reducing the ‘pain
points’ …”
JAMIE DIMON
JPMorgan Chase & Co.
Chairman & Chief Executive Officer
April 2015
This slide courtesy Andy Brown, Sandhill East
https://www.linkedin.com/in/andybrown63/

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go digital
or die trying

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gs wants to
become "google
of wall st."

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stanford
PhD
CIO CFO
marquee
data analytics data
api
api
monitor
observe
analyze

22. THE REALLY BIG PICTURE
2020 2021 2022 2023 2024
In 5 – 10 years, every
company will be a
Digital Software
Business
Security, service
assurance and consumer
centricity become THE
BOARD LEVEL
PRIORITY
Enterprises going
DIGITAL ADOPT
HYBRID IT
This slide courtesy Andy Brown, Sandhill East
https://www.linkedin.com/in/andybrown63/

23. 40% Change 60% Run
Infrastructure Led
Owns Facilities, Data Centers,
Hardware, Networks et al
Has Refresh Cycles caused by
Capital Depreciation
Still using Waterfall for App
Dev
Thinking led by Inf
Technologists
(hardware, DB, OS et al)
Traditional Procurement
Less Agile, Change resistant
60% Change 40% Run
AppDev starting to lead
Owns less Facilities, Data
Centers, Hardware,
Networks, et al
Still Has Refresh Cycles
caused by
CapitalDepreciation
Combination Waterfall &
Agilefor App Dev
Thinking led by CIO “Move to
Cloud” Traditional
Procurement weakening
More Agile, Less Change
resistant
80% Change 20% Run
AppDev leads decisioning
Doesn’t own hardware
Refresh doesn’t exist
All Agile for App Dev
Thinking led by CIO “Move to
Cloud”
Cloud Centric “Marketplace”
Procurement
Embraces Change, Very Agile
traditional hybrid digital
This slide courtesy Andy Brown, Sandhill East
https://www.linkedin.com/in/andybrown63/

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2045
?

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SNMP / traps
or
Daylight Savings

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AMRS
EMEA
APAC
EPS
every ip interface globally
AIOps
EdgeOps
EdgeOps
EdgeOps

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algorithms we
use

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This slide courtesy our Chief Scientist Dr. Rob Harper -- Do check out his great 3-part blog on Machine Learning in Moogsoft AIOps: https://www.moogsoft.com/author/robharper/

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By Hui Li on Subconscious Musings April 12, 2017

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This slide courtesy our Chief Scientist Dr. Rob Harper -- Do check out his great 3-part blog on Machine Learning in Moogsoft AIOps: https://www.moogsoft.com/author/robharper/

31. NOT FOR GENERAL DISTRIBUTION ONLY INTENDED FOR REGISTERED ATTENDEES OF QCON SF 2017
This slide courtesy our Chief Scientist Dr. Rob Harper -- Do check out his great 3-part blog on Machine Learning in Moogsoft AIOps: https://www.moogsoft.com/author/robharper/

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regression classification clustering
This slide courtesy our Chief Scientist Dr. Rob Harper -- Do check out his great 3-part blog on Machine Learning in Moogsoft AIOps: https://www.moogsoft.com/author/robharper/

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classification supervised
“learn by example” approach. Supervised learning systems need to be given examples of what is “good” and
what is “bad”
This slide courtesy our Chief Scientist Dr. Rob Harper -- Do check out his great 3-part blog on Machine Learning in Moogsoft AIOps: https://www.moogsoft.com/author/robharper/

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classification

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clustering unsupervised
Patterns that you didn’t know existed prior. Recommender systems rely heavily on these techniques.

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supervised machine learning "hot dog?" "not hot dog?"

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algorithms we
use

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lua code: https://pastebin.com/ZZmSNaHX
SethBlingmari/o
neural nets
https://www.youtube.com/watch?v=qv6UVOQ0F44

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k-means clustering

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matrix factorization

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shannon entropy

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typical entropy distribution

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algorithmic
workflow

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millions events
de-duplication
cluster analysis
algorithms
situation room
teams-centric
thousands of alerts
algorithmic
noise filtering
[shannon entropy]
tens of alert clusters
(situations)
"today's warnings are
tomorrows outages"
"all about the MTTR"
algorithmic
probable root cause
AIOps
Algorithmic IT Operations
knowledge capture
auto-recurrance detect
entropy_threshold
non-noisy alerts
what you're likely
doing today
L1 "Catch & Dispatch"
(automated)
ignore
situation next steps

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...speaking of classification

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fault vs audit
fix → optimize

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monitoring fail-around
analytics analytics
fail-around fail-around
monitor

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weld the datacenter doors shut

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<lofty_tangent>
non-technical

51. THE
INDUSTRIAL
REVOLUTION
1.0

55. WE FACE
EXISTENTIAL
THREATS
TO OUR PROGRESS

57. THE
INDUSTRIAL
REVOLUTION 2.0
CAN HELP SAVE US

58. v1.0 v2.0

59. COMPLEXITY IS
THE PRINCIPAL
THREAT
TO THE REVOLUTION

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</lofty_tangent>
non-technical

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theory
applied
sharing | giving back

65. Watch the video with slide
synchronization on InfoQ.com!
https://www.infoq.com/presentations/
modern-architectures-data-science