AIOps or Artificial Intelligence based IT operations is the buzzword that’s capturing the CXO’s interest in organizations worldwide.

1. The Future of AIOps

2. ©2018 GAVS Technologies Confidential 2
o AIOps - Latest IT Operations Trend
o Current Business IT Operations Scenario
o AIOps is the Future
o Practical Use Cases of AIOps
o Conclusion
o About GAVS
Table of Contents

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AIOps - The Latest IT Operations Trend
A term coined by Gartner, AIOps or Artificial Intelligence based IT operations is the merging of IT
Service Management (ITSM), IT Operations Management (ITOM) and IT Automation at the data
layer.
AIOps platforms
o Software systems that combine big data and artificial intelligence (AI) or machine learning
functionality
o Data must reside in a Big Data platform that supports the application of real-time analytics as
well as deep historical queries

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AIOps - The Latest IT Operations Trend (contd.)
AIOps platforms
o The analytics must be managed by machine learning that supports both supervised and
unsupervised processing as the data streams in
o Enhance and partially replace a broad range of IT Operations processes and tasks
o Examples includes
- Availability and Performance monitoring
- Event correlation and analysis
- IT service management
- Automation

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Current Business IT Operations Scenario
AIOps or Artificial Intelligence based IT operations is capturing the CXO’s interest in organizations
worldwide.
Why?
o Traditional tools and processes inability to manage the data explosion right from its creation, storage,
analysis and management
o Humans inability for thorough analysis to obtain any meaningful insights.
o Speed, security and reliability challenges for IT teams in a mobile and connected world.
o Complex, manual and siloed processes of the legacy IT solutions.
Results
o Low IT productivity as they unable to find the exact root cause of incidents.
o Business leaders don’t have a 360-degree view of all their IT and business services across the
organization.

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AIOps is the Future
AIOps platforms are the foundation on which the organizations will project their future
endeavours.
o Uses advanced ML and analytics to enhance IT operations through a proactive approach
towards service desk, monitoring and automation
o Provide insights to impact business decisions through effective data collection methods
o Utilize real time analytic technologies

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AIOps is the Future (contd.)
AIOps delivers a predictive and proactive IT by decreasing
o MTBF (Mean time between failure)
o MTTD (Mean time to detection)
o MTTR (Mean time to resolution)
o MTTI (Mean time to investigate) factors.
Successful AIOps implementations depend on key parameters Index (KPIs) which impacts
o Performance variation
o Service degradation
o Revenue
o Customer satisfaction
o Brand image

8. Practical Use Cases of AIOps
Capacity planning
o Enterprise workloads are moving to the cloud providers such as AWS, Google and Azure for
setting up various configurations
o Complexity increases as new configurations for disk types, memory, network and storage
resources are added
o Reduces the guesswork in aligning the correct usage of the network, storage and memory
resources with the right configurations of servers and VMs.

9. Practical Use Cases of AIOps (contd.)
Optimal resource utilization
o Leverage cloud elasticity to improve their application scaling automatically
o IT administrators can rely on predictive scaling to take the auto scale cloud to the next level
o Based on historical data, automatic scaling of resource through self monitoring

10. Practical Use Cases of AIOps (contd.)
Data store management
o Utilized to monitor the network and storage
resources that impacts IT applications in the
operations.
o Event notification for any performance
degradation issues
o Automation of mundane network and storage
management tasks such as reconfiguring and
recalibration
o Automatic and proactive storage capacity through
predictive analytics

11. Practical Use Cases of AIOps (contd.)
Anomaly detection
o Prevent potential outages and disruptions
o Pinpoint anomalies in any part of the technology stack in real-time, using advanced analytics
and machine learning
o Accurately detect the actual source of anomaly helps IT teams to perform efficient RCA almost
in real-time.

12. Practical Use Cases of AIOps (contd.)
Threat detection & analysis
o Enhance the security of existing IT infrastructure
o IT Security systems uses ML algorithms and AI’s self-learning capabilities to detect data
breaches and violations
o Detect anomalies and risk events by correlating various internal sources like log files, network
and event logs, with the external information on malicious IPs and domains
o Advanced machine learning algorithms used to identify unexpected and potentially
unauthorized and malicious activity within the infrastructure

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Conclusion
Although still early in deployment, companies are taking advantage of AI and machine learning
to improve tech support and manage infrastructure. AIOps, the convergence of AI and IT ops,
will change the face of infrastructure management.

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About GAVS
GAVS Technologies (GAVS) is a global IT services & solutions provider enabling digital
transformation through automation-led IT infrastructure solutions. Our offerings are powered by
Smart Machines, DevOps & Predictive Analytics and aligned to improve user experience by 10X and
reduce resource utilization by 40%.

15. www.gavstech.com
inquiry@gavstech.com