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• @marcocoulter
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Marco Coulter - Technical Evangelist, AppDynamics@marcocoulter
Jesse Haraldson - Sr. Software Architect, Thomson Reuters
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AIOps – the Life Preserver for ITOps
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Reality check: which quote best matches you?
• “We react to operational events each time they happen”
• ”We proactively learn from past events to improve our processes”
• “We use data to predict imminent events”
• “We use data to preemptively optimize our outcomes”
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Full report at: http://ow.ly/kI1X30nlXkc or
https://connectedfutures.cisco.com/report/transforming-it-operations-for-business-value/
27% 33%7%
26% 38% 22% 14%
33%
Reactive Proactive Predictive Preemptive
Today
In two years
Self-assessment:
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What if you never had to react to outages again?
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• A Reality Check - Machine Learning 101
• The AIOps Benefit
• Guest Business Speaker: Jesse Haraldson from Thomson Reuters
• Your Central Nervous System
• Deciding What to Automate
Agenda
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Questions? Use Cisco Webex Teams to chat with the speaker after the session
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Cisco Webex Teams
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Let’s talk about Machine Learning
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Artificial ‘General’ Intelligence AI/ML
Promise vs. Reality
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T R A D I T I O N A L , R U L E S - B A S E D
Live data
Program Output
Traditional Programming vs. Machine Learning
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T R A D I T I O N A L , R U L E S - B A S E D M A C H I N E L E A R N I N G
Training data
Output Program (Model)
Live data
Program Output
Traditional Programming vs. Machine Learning
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Instance (Observation, Record)
Feature (Attribute, Predictor) Objective (Target)
Basic Terminology
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y = a + bx
Example:
A model to predict the selling
price (y) of a home using simple
linear regression algorithm
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Read the data
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Prepare the data
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Correlate the data to find our x
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Now we have x, we still need a and b
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Train the model (derive a and b) and test
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Visualize the result (for fun and confirmation)
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Lots of dots becomes blue line, because mathematics!
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AI/ML is not magic, it is just fast!
ML finds patterns and
derives conclusions at speeds
humans aren’t capable of.
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About this life preserver … umm, what exactly is AIOps?
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AIOps augments technology teams by leveraging AI/ML for real-time anomaly detection and insights, identifying root causes and automating resolutions.
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What is Normal? Goodbye Manual Thresholds!
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AIOps: Correlate Network Metrics
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AIOps: Automated Root Cause Analysis
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AIOps: Detect Business Anomaly
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#CLUS
Jesse Haraldson – Principal ArchitectFindLaw – A Thomson Reuters Business
AIOps Essentials Theater
AppDynamics on a Consumer Content Property
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FindLaw – Get Help With All of Your Legal Needs
Increase awareness of your
firm in your community
Create an engagement path leading directly to
your law firm
Meet the needs of your practice and your prospective
clients
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Pre-AppDynamics Placemat
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Post-AppDynamics Placemat
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Challenges – Insights from IT-level to Business Outcomes
Reduce Lost Leads
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Realize operational efficiencies
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Ensure end-to-end performance
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AppDynamics Ensures Leads Get through the Funnel
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Downtime is Costly
400 People ImpactedAvg. Hourly Rate: $35
$14,000 an hour issue
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AppDynamics Ends the War Room
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AppDynamics Makes Third-party Services’ Performance Transparent
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AppDynamics Focuses on What Matters Most
Cutover
Cutover
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AppDynamics Provides Complete Visibility
CPU Usage
Cutover
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What’s Next?
• Move from measuring transactions to measuring impact
• Look for more integrations with strategic vendors
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Central Nervous System
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RCA
Publiccloud
Privatecloud
App and business
InsightVisibility
Tim
e s
eries
and e
vents
data
Orc
hestr
ation
Action
Feedback Loop
Incident response
Resource scaling
Cost optimization
Network provisioning
ML/AIAlerting API
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Action
Publiccloud
Privatecloud
Visibility
Correlation engine
Unified dashboard
ML/AI
Insight
Tim
e s
eries a
nd e
vents
data
Orc
hestr
ation
App and business
Infrastructure
Network
Security
Feedback Loop
Incident response
Resource scaling
Cost optimization
Network configuration
Security enforcement
The Central Nervous System
Alerting API
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Freedom to innovate by
automating everyday tasks
• Continuously adapt to changing business conditions
• Automation driven by cross domain insight
• At your own pace
Autonomous action
Orc
hestr
atio
n
Incident response
Resource scaling
Cost optimization
Network configuration
Security enforcement
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AIOps use case: intelligent auto-scaling
Create change request to create additional VMs
5UCS Director
Feedback loop shows improvement which automatically closes the incident and archives slack channel7
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Performance anomaly detected
Correlate application performance with
network and infrastructure to determine
root cause and correct action
+
2
Visibility Insight Action
3 Create incident in ServiceNow
6Puppet deploys correct application version to new VMs
Automatically create slack channel and invite the right people
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Preemptive“We use data to continuously optimize our outcomes”
Predictive“We use data to predict and respond to imminent events”
Reactive“We react to operational events as they happen”
Proactive”We learn from past events to improve our processes
Four Steps to Operations Nirvana
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• AIOps offers immediate relief to increasing engineering burden
• Better with cross domain
• Better with business transaction
• Automate the complex & mundane
• Augment yourself!
AIOps – the Life Preserver for ITOps
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Questions & Links
• Email [email protected] for a copy of the presentation.
• Our Vision for AIOps - https://blog.appdynamics.com/aiops/aiops-central-
nervous-system/
• Staffing IT for AIOps -
https://www.forbes.com/sites/appdynamics/2019/03/11/is-your-it-
workforce-ready-for-aiops/#5ff583f2b005
• Blog posts on AIOps - https://blog.appdynamics.com/aiops/
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What’s Next
AppDynamics Visibility Pack
Demos in the Business Insights village
1:1 meetings with our experts
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Thank you
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• @marcocoulter
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• @marcocoulter