Presentation slides from the Splunk ITOA roundtable event that took place in Zurich, November 2017. Attendees learnt: - What is machine learning - Why machine learning is critical for today's IT - The challenges you will need to overcome - Some real examples of machine learning use cases - How to get started to machine learning

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ITOA Roundtable
30.11.2017

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▶ 09:00 – Breakfast
▶ 09:30 – Niklaus Seiler to kick off
▶ 09:35 – Round table introductions.
▶ 10:00 – Presentation of ML and AIOps
▶ 10:30 – Break
▶ 10:45 – Round table discussion
▶ 12:00 – Lunch
Agenda

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Five things you will learn today
What is Machine Learning
Why ML is critical for today’s IT
The challenges you will need to
overcome
Some real examples of
ML use cases
How to get started doing ML
1
2
3
4
5

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Splunk turns
machine data
into answers
Splunk’s trusted analytics platform
empowers people to dive into their
machine data so they can find answers
quickly and see opportunities in real-time.

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Machine Learning
Deep Learning
Artificial Intelligence
Neural Networks
Unsupervised
Supervised
What does it
mean?

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Machine learning used in our
everyday lives
Facial
Recognition
Product
Recommendations
Natural Language
Processing
Self Driving
Cars

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AI
Machine
Learning
Deep
Learning
AI, Deep Learning, And Machine Learning
Intelligent Agents
No Human Involvement
Sentient Machines
Tensorflow
Data sets
are large and
unknowable
Guided Data Driven Decisions
Augmenting Human Reasoning
Operational
Intelligence
Splunk ML offerings today
Neural Networks

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Three Types Of Machine Learning
Supervised Learning:
Unsupervised Learning:
Reinforcement Learning:

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Deviation from past behavior
Deviation from peers
(aka Multivariate AD or Cohesive AD)
Unusual change in features
ITSI MAD Anomaly Detection
Predict Service Outages
Predicting Churn
Predicting Events
Trend Forecasting
Detecting influencing entities
Early warning of failure – predictive
maintenance
Identify peer groups
Event Correlation
Reduce alert noise
ITSI Event Analytics
Anomaly detection Predictive Analytics Clustering
Typical ML use cases in Splunk

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Custom Machine Learning – Success Formula
Domain
Expertise
(IT, Security, …)
Data
Science
Expertise
Splunk
Expertise
Identify use cases
Drive decisions
Set business/ops priorities
SPL
Data prep
Statistics/math background
Algorithm selection
Model building
Splunk ML Toolkit
facilitates and simplifies
via examples & guidance
Operational success

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▶ Assistants: Guided model building, testing
and deployment for common objectives
▶ Showcases: Interactive examples for typical
IT, security, business and IoT use cases
▶ Algorithms: 25+ standard algorithms included
with the toolkit
▶ ML Commands: New SPL commands to
fit, test and operationalize models
▶ Python for Scientific Computing Library:
Access to 300+ open source algorithms
Splunk Machine Learning Toolkit
Extends Splunk platform functions and
provides a guided modeling environment
Build custom analytics for any use case

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Demo

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Machine Skills
• Creative
• Design
• Empathy
• Intuition
• Sales
• Marketing
• Business Acumen
• Analyzing high-velocity
data
• Analyzing big data sets
• Complex data sets
• Predicting future values
• Detect Anomalies
• Complex patterns
Human Skills
Human and machines

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How long do you spend driving a
car?
How much of that time is
productive?
What’s the value?

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How much time do we spend
doing this in IT?
Searching lots of data
Creating static rules
Monitoring
Troubleshooting
Root Cause Analysis
Capacity Planning
War Rooms
Forecasting
….

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Current Challenges that need to be addressed
Lower customer
satisfaction
High cost of IT
Operation
Inefficient use of
resources
Lost revenue
Most of IT budget is
spent operating
datacenters
$426 Billion in
downtime per year
300% increase in
number of IT events
over last 5 years
Drop in customer
satisfaction largely
due to issues with
digital channels

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Analysing large sets of data
Fast-flowing data
Complex data
Spotting complex patterns
Reacting in real time
And yet we said that machines are good at
What is stopping us from doing ML for IT operations?

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Collecting and analysing this data has never been so easy
Network
InfrastructureLayer
Packet, Payload, Traffic,
Utilization, Perf
Storage
Utilization, Capacity,
Performance
Server
Performance, Usage,
Dependency
ApplicationLayer
User Experience
Usage, Response Time,
Failed Interactions
Byte Code Instrumentation
Usage, Experience,
Performance, Quality
Business Performance
Corporate Data, Intake,
Output, Throughput
Splunk Approach:
▶ Single repository for ALL data
▶ Data in original raw format
▶ Machine learning
▶ Simplified architecture
▶ Fewer resources to manage
▶ Collaborative approach
MACHINE
DATA

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1 2 3 4 5 6
How Splunk helps with Machine Learning
Splunk Splunk ML Toolkit
Third party
Data
Collection
Data
Cleansing &
Preparation
Analysis &
Feature
Extraction
Create
Models
Refine
Models and
Algorithms
Put into
Production
Splunk Apps with ML

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AIOps
Common technologies and data sources in use today

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Album: Honeymoon
Common characteristic: Water
Album: Honeymoon
Common characteristic: Car Noise/Junk
Album: Safari
Common characteristic: Wildlife

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Demo

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What we just saw…
Adaptive Thresholds Anomaly Detection Event Correlation
Manage and maintain KPI thresholds by dynamically adapting to changing operational patterns
Catch issues that thresholds can’t—baseline normal operations and alert on anomalous conditions
Reduce event clutter, false positives and rules maintenance by auto-grouping related events

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▶ Modelling on ITSI Services and KPIs
▶ Predictive Analytics for future Service Health Scores
▶ Proactive alerting integrated into the ITSI notable events framework
▶ Identify leading indicators for possible service degradation
Predict Service Health Score and
Prevent Outages with Machine Learning
https://www.splunk.com/blog/2017/08/28/itsi-and-sophisticated-machine-learning.html

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Machine
Learning on
Events at
the World
Bank

27. Thanks to the integrated machine learning
in Splunk ITSI, we now have a reduced
number of events to process and the
streamlined event analytics framework
allows us to process events eight minutes
more quickly
Laurent Amouroux,
Technical Director
Econocom Infrastructure Management Services
15% increased
SLA Performance
60% reduction
In number of events
10x reduction
in number of system
performance events through
machine learning

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Outlier detected for faulty cell tower light
The tower light turns on only at night,
controlled by Canadian air traffic control

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This aligns directly with a company’s top priorities
Drive revenue
95% passengers through
security >5 mins
> spending more time
shopping
High inventory waste and
food going stale
> immediate sales
insight
Lower cost
Hard to get threat
insights
> real-time security
response
Reduce
risk
Negative impact of outage
at peak times
> improved business
& customer insight
Customer experience

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Where do I
start?
Better manage your
alerts generated by
your IT systems with
ML
Apply adaptive
thresholds with ML to
remove time
configuring static rules
Build a model to
predict service
outages
Do not go big
bang – start
simple and
prove value
very quickly
Faster
detection of
IT incidents
IT teams
more
productive
Prediction of
failures

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Open Discussion

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Thank you.