What is AIOps?

Originally coined by Gartner, AIOps stands for “Artificial Intelligence for IT Operations”. Think of it as using AI to help manage and support IT workflows.

What does that mean in practice? First, an AIOps deployment might collect diagnostic data and activity logs from a range of systems and services. It then analyses it using discriminative AI to generate alerts and forecasts for the IT team. (We cover discriminative AI in our guide to generative AI.)

Depending on the configuration, the AIOps may also trigger automated responses to particular scenarios.

Table of contents

• What problem does AIOps solve?
• What is the difference between AIOps and DevOps?
• What is the difference between AIOps and MLOps?
• What is the difference between AIOps and RPA?
• Is AIOps cloud based?
• What is observability in AIOps?
• What is anomaly detection in AIOps?
• How do I start learning AIOps?
• What is the future of AIOps?

What problem does AIOps solve?

Businesses rely on an increasingly diverse set of technologies, with workers using different software platforms in different locations. At the same time, reliability and quality of service are more important than ever. An AI can apply “big data”-style analysis to very large volumes of data and identify issues with greater speed and precision than human staff.

What is the difference between AIOps and DevOps?

DevOps is a development methodology where coders work in close partnership with the IT department to streamline the processes of software creation, deployment and maintenance. AIOps isn’t about development: it focuses on the smooth running of the IT infrastructure and can be deployed and operated entirely within the IT department.

What is the difference between AIOps and MLOps?

AIOps relies partly on machine learning (ML) to understand the configuration and operation of a company’s systems. However, the term MLOps specifically refers to applying a DevOps model to the deployment and management of ML systems. It’s quite different to AIOps, which is about using AI for general IT operations.

What is the difference between AIOps and RPA?

RPA stands for robotic process automation; an example might be a software script that automatically performs certain tasks or responds to specific triggers. This type of automation is found in all sorts of businesses, but RPA only works with defined inputs and processes. AIOps uses more advanced learning and analysis technologies, allowing the AI to discover significant patterns and insights for itself – although it may initiate an RPA activity in response to particular events.

Is AIOps cloud based?

AI requires advanced software and considerable computing power, so the most practical way for most companies to take advantage of it is by drawing on commercial cloud-based AI services. Both Amazon’s DevOps Guru and Microsoft’s Azure platforms include extensive AIOps functions, while IBM’s Instana and SevOne products offer AI-powered observability and network performance monitoring respectively.

What is observability in AIOps?

Observability is a key benefit of AIOps. The term refers to the ability of an AIOps system to provide a comprehensive overview of the health of a network and applications. While regular network monitoring software can provide a degree of observability, the AI system can analyse and correlate data from the widest possible range of sources, to provide deep insights that a simpler approach might miss.

What is anomaly detection in AIOps?

Anomaly detection is another core function of AIOps. This refers to the ability of the AI to identify deviations from the norm that could indicate problems or bottlenecks, and raise the alarm appropriately. While regular monitoring systems can issue alerts when a monitored value passes a preset threshold, the AI is able to learn what the “ordinary” operation of a network looks like and detect subtle changes that would be missed by regular tools.

What is the sequence of phases in implementing AIOps?

There’s no one-size-fits-all game plan for AIOps: every business has different needs and priorities. However, deployment and use can be approached as a three-step process:

1. Planning: Businesses need to identify what they want from an AIOps system, figure out what data they will need to feed it to achieve that goal, and ensure they can provide that data in a usable form.
   
2. Implementation: Once the AIOps service is up and running, it will start to learn about the business’ systems. IT staff can monitor its reports and predictions to extract actionable insights.
   
3. Automation: Once familiarity and trust are built up, automations can be deployed, such as allowing the AI system to spin up extra cloud capacity if it detects that a virtual server is in danger of becoming overloaded.

How do I start learning AIOps?

Working with AIOps calls for expertise across the fields of AI systems and IT management. To obtain useful output from your AIOps system, you need to use IT skills to select meaningful data to be analysed, prepare that data for machine-learning processes, and manage its ongoing flow.

A good knowledge of network and systems administration is also required to understand and respond appropriately to the information produced by the AIOps platform.

What is the future of AIOps?

AIOps is growing in popularity. Gartner projects 15% annual growth for at least the next three years, implying that AIOps will become available to an increasing number of businesses, and cover a wider range of computer systems.

Adoption will be helped by updates to conventional IT services and monitoring systems that help them integrate better into AIOps environments.

It’s also expected that the scope of AIOps automation will grow, with AI systems taking over more of the business of day-to-day infrastructure management. This promises to reduce the demands on skilled IT staff and minimise downtime and disruption when errors arise.