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• @marcocoulter

Marco Coulter - Technical Evangelist, AppDynamics@marcocoulter

Jesse Haraldson - Sr. Software Architect, Thomson Reuters

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AIOps – the Life Preserver for ITOps

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%

Reactive Proactive Predictive Preemptive

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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Let’s talk about Machine Learning

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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101000 0110 0

11000011100 1

1001 1101 111

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?

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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Jesse Haraldson – Principal ArchitectFindLaw – A Thomson Reuters Business

AIOps Essentials Theater

AppDynamics on a Consumer Content Property

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

Realize operational efficiencies

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

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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

Publiccloud

Privatecloud

App and business

InsightVisibility

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

Insight

eries a

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

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

Performance anomaly detected

Correlate application performance with

network and infrastructure to determine

root cause and correct action

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 evangelism@appdynamics.com 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

• @marcocoulter

@marcocoulter · Find this session in the Cisco Live Mobile App Click “Join the Discussion”...

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