How Does AIOps Benefit DevOps Pipeline and Software Quality? - DevOps Next

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How Does AIOps Benefit
the DevOps Pipeline and
Software Quality?
E R A N K I N S B R U N E R

3 | DevOps Next 2020 perforce.com
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The information contained in this document is strictly confidential, privileged, and
only for the information of the intended recipient. The information contained in this
document may not be otherwise used, disclosed, copied, altered, or distributed
without the prior written consent of Perforce Software, Inc.

ABOUT ME:
Eran Kinsbruner
• Chief Evangelist, Product Manager, and Author at Perfecto by Perforce
• Blogger, Inventor, and Speaker
• 20+ years in software development & testing
• Author of:
• The Digital Quality Handbook
• Continuous Testing for DevOps Professionals
• Accelerating Software Quality
• Twitter: @ek121268

Today’s Agenda
1
2
Introduction and Importance of AIOps in DevOps
Key Pillars of AIOps Solutions
3 Quantifying the Value of AIOps
Q&A5
4 Future of AIOps in DevOps

AIOps, (artificial intelligence for IT operations) is the application of artificial intelligence (AI) to enhance IT operations.
Specifically, AIOps uses big data, analytics, and machine learning capabilities to do the following:
• Collect and aggregate the huge and ever-increasing volumes of operations data generated by multiple IT
infrastructure components, applications, and performance-monitoring tools.
• Intelligently sift ‘signals’ out of the ‘noise’ to identify significant events and patterns related to system performance
and availability issues (detect business anomalies).
• Automatically diagnose root causes and report them to IT for rapid response and remediation — or, in some cases,
automatically resolve these issues without human intervention.
Introduction to AIOps

• Enhance organizations digital transformation
• Achieve faster MTTR (focus on real issues instead of noise)
• Go from reactive to proactive to predictive management (predictive alerting)
• Modernize your IT operations and your IT operations team (focus on innovation rather then KTLO)
AIOps Benefits
Source: Nextel Case Study

AIOps Objectives
Source: AppDynamics

AIOps – Predict and Prevent Ops Issues With ML
Source: Splunk

• Automated pattern discovery
and prediction
• ZIF uses ML and predictive
techniques to identify an event that
can turn into a high severity incident
even before it reached the service
desk agent
• ZIF can perform Level-0 Triaging
of incidents and assign it to the
appropriate desk agent
• AI voice
assistance
AIOps Example
Source: Gavstech

AIOps Solution Example
Source: https://www.validatek.com/technologies/ai-ops
• Key Features:
• Event correlation
• Noise reduction (filters, blacklisting,
etc.)
• Collaboration between security and
operations
• Ecosystem automation (auto-
ticketing, auto-diagnostics,
escalation, etc.)
• Flexible implementation options
(SaaS, on-premise, hybrid)
• Integration with third party tools
(Splunk, Remedy, SolarWinds)

• Historical analysis of issues (e.g. service tickets).
• Current and older performance analysis.
• High-risk product anomalies that were detected.
• Outages and other service-related issues.
• Key user journey analysis.
• Pattern discovery and correlation.
• Market-specific KPIs and analytics (benchmarks).
• Product specific inputs.
AIOps Input Data

AIOps Maturity Model and Components
Science Logic Definition of AIOps and its Maturity Model https://sciencelogic.com/solutions/aiops

APM + ITIM + ITSM =AIOPS
APM – Application Performance Management
ITIM – IT Infrastructure Monitoring
ITSM – IT Service Monitoring

AIOPS Within DevOps Processes
Source: Medium.com

APM – Current State of the Market
Anomalies detection, grouping, & correlation Real-time proactive detection

APM – Current State of the Market
Incident Intelligence

• Builds leave a trail of unnecessary
files behind them, which can
quickly amount to TBs of data. A
cleanup script was created to scan
through the build data and remove
these files, and the team built a
dashboard to monitor its activity —
the amount of data cleaned as well
as the processes left behind.
ITIM – Current State of the Market

ITIM – Current State of the Market

ITSM – Current State of the Market
Incident management for managing and
tracking incidents
Problem management for managing problem
investigations from detection to eradication,
through the ITIL subprocesses of problem
control, error control, and proactive problem
analysis.
Change and release management for tracking
scheduled and planned infrastructure
changes.
Service-level management for tracking
service-level commitments with customers
and from vendors, so management can
pinpoint weaknesses and take corrective
action.

ITSM – Current State of the Market

Future of AIOps
• Integral part of DevOps and IT management
• Shorter DevOps and production issues cycles
• Higher code quality continuously, more emphasis on shift-right processes
• Better cross-function collaboration (Dev, QA, Ops, Business)

How Does AIOps Benefit DevOps Pipeline and Software Quality? - DevOps Next

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