Enterprise AIOps:
How ManageEngine refines IT processes with artificial intelligence
Chapter 1: Introduction
Can you imagine your iPhone without Siri? Or traveling to a new city without Google Maps? AI is an integral part of our daily lives. Thanks to big data and massive computing power introduced in the 2010s, AI started making its way into IT operations. We now utilize AI in solutions like chatbots and object recognition tools. In 2016, Gartner coined the term artificial intelligence for IT operations (AIOps) to "combine big data and machine learning (ML) to automate IT operations processes."
AIOps could be a massive boost to help an organization's IT move at the speed of business—when implemented the right way. ManageEngine's venture into AIOps was a natural outcome of our efforts in automating our IT processes. In 2015, we accelerated our efforts by establishing a dedicated research team for AI. We started integrating AI into our solutions to streamline our own IT processes, and we will continue to solve our problems and provide these AI-enhanced solutions to our customers.
In this e-book, we'll look at how ManageEngine leveraged AIOps to refine IT operations and craft better solutions. Read on to gain deeper insights on:
- Objectives that set the tone for our AI ventures
- Our core technology for AI
- How we used AI to improve service management
- Applying AI to build a resilient IT environment
- Endpoint delivery with AI
ManageEngine's approach to AIOps
Deep R&D has been a key area of focus for years. Although we established our in-house artificial intelligence team in 2015, our approach has been to introduce AI to support our IT processes. After establishing a dedicated team for AI, we expanded the breadth of our work and made tremendous strides in this field. Here's a summary of our AI initiatives:
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Early AI initiatives |
Current AI initiatives |
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Performing accurate predictions, forecasting, and capacity planning for a wide range of applications |
Detecting dependencies using knowledge graphs |
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Managing incidents better by grouping alerts, analyzing incidents live, recognizing patterns, and predicting upcoming events |
Tracing root cause of incidents with causal inference |
Our objectives and technologies help us stay on track with our AI ventures.
Our AIOps objectives
To ensure our AI initiatives are aligned with user experience and our inherent organizational values, we established our AIOps objectives:
Explain first, improve next:
Explainable AI ensures the users trust the AI system first, as they know how it makes decisions. Our explanation-first approach helps create an acceptable AI model and improve user experience.
Minimize data:
With privacy regulations around the world bringing in lower data retention periods, we've trained our AI system to perform well even with a limited amount of data.
Be proactive:
Our AI system foresees the conditions and takes action when the IT environment meets certain conditions. It recommends possible actions while confronting an incident.
Privacy first:
End users' privacy has become paramount over the past few years. Our models are trained to prioritize privacy.
Filter out the noise:
Our AI system cleans the data sets before making a decision to improve the accuracy of predictions.
Embrace change:
In this ever-changing IT landscape, we have modeled our AI systems to adapt to changes as quickly as possible.
Our technologies
Our technologies have evolved over the past few years as we take on more AIOps challenges. Here are the three fundamental technologies that power our AIOps engine:
Statistical machine learning:
It combines statistical methods with machine learning to analyze data better and provide accurate insights. We use SML in areas like anomaly detection, outage prediction, and root cause analysis.
Computer vision (CV):
Our AI system uses CV to gain meaningful insights from various digital inputs to take meaningful actions, or make recommendations. We use CV in areas like object recognition.
Natural language processing (NLP):
We use NLP to help our AI system understand human interactions better. NLP is useful to facilitate self-service, detect the sentiment of users, and gather deep insights.
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