What is AIOps? Artificial intelligence for IT operations explained

AIOps, short for “artificial intelligence for IT operations,” is an approach combining big data analytics and machine learning to automate and streamline IT tasks. Developed in 2016 by Gartner, an American technology and consulting company, AIOps uses technologies like natural language processing and machine learning. It analyzes vast amounts of data to improve IT processes and detect and address anomalies in real time.

Intelligence for IT operations uses artificial intelligence, a technology inspired by the workings of the human brain — only quicker and not prone to human error. Here’s how AIOps uses AI to improve IT operations:

Data collection

AIOps gathers a lot of information from different parts of the IT system, such as ever-growing logs and performance data. AIOps tools gather data in real time to provide the most up-to-date information.

Data organization

AIOps combs through this data, picking out relevant information and ignoring the remaining “noise.” This helps analyze the overall IT infrastructure performance.

Data analysis

Using AI, which mimics human thinking, AIOps recognizes patterns and connections in data and compares them to others. AIOps quickly detects any deviations from the norm, allowing employees to respond quickly. It also prioritizes the most serious events for immediate response.

Providing insights

The AIOps analysis helps staff understand the current state of the IT system and spot problems. Artificial intelligence can also predict future issues based on past data, helping prevent problems before they become reality.

The AIOps technology is becoming more and more popular in various organizations. No wonder – it offers a lot of advantages for IT operations:

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  Increased data usefulness. AIOps turns gigabytes of dry information into patterns and relationships, which are far more meaningful than raw data.
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  Cost reduction. Analyzing data with AIOps is cheaper than employing many people to do the same job.
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  Increased team productivity. While AIOps deals with mundane and repetitive issues, experts have time to tackle more serious tasks.
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  Improved decision-making. By analyzing data, AIOps gives valuable insights, helping IT teams make better-informed choices.

All these fast results and low costs come at a price. That’s the case with any AI-based tool, including AIOps.

Here are some of AIOps’ drawbacks:

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  Data security risks. AIOps collects and analyzes massive amounts of data. Although it makes people’s work easier, it also poses a threat. Hackers love getting their hands on such large amounts of data, and AIOps systems can fall prey to attacks and abuses. It’s crucial to secure data with strong encryption, train employees, and regularly update the systems.
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  Risk of incorrect insights. The quality of the interpretation provided by AIOps depends on the quality of the input data. If the data is outdated or contains errors, AIOps won’t offer useful insights. AIOps needs correct data gathered in real time (preferably through automated tools) to work as expected.
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  Difficult implementation and management. Integrating AIOps into existing IT infrastructure can be challenging. It often requires significant changes to systems and processes, as well as training for staff, making it a complex project.

AIOps tools are mainly used in organizations, where they play an important role in managing IT operations:

Anomaly detection

AIOps helps identify anomalies and unusual activities in real time. It uses historical data to understand what normal events look like and compares current behavior to these patterns. The system notifies teams if it finds something significantly different from the norm. This reduces the time it takes to detect and resolve such anomalies.

Data analysis

Teams rely on meaningful correlations and trends that AIOps derives from event data. This information helps them understand how their IT infrastructure is performing and see connections between events. It also gives them the knowledge to make informed decisions and plan for infrastructure development.

Predictive analytics

AIOps uses historical data and correlations to predict events that may occur in the future. It helps prevent potential problems such as exceeding capacity limits or performance degradation. IT teams supported by AIOps can strategically address these issues in advance.

Process automation

Organizations use AIOps to automate tedious and time-consuming tasks. AI collects and analyzes data, alerts staff to unusual behavior, and handles minor issues on its own. It frees the staff of the most mundane tasks and allows them to take care of the more complex or pressing issues.

Root cause analysis

IT teams use AI-based solutions to understand the correlations between events. This knowledge helps them trace problems back to their original source. AIOps helps identify root causes and makes the entire process faster and more efficient.

DevOps adoption

AIOps works well with DevOps, a method that combines software development and IT operations. It helps make the process of implementing security measures in software development smoother.

FAQ

Why is AIOps important?

AIOps is important because it uses data analytics to find and solve IT problems, and its predictive abilities help fix issues before they happen. It makes managing and protecting IT infrastructure easier.

What are the four key stages of AIOps?

The four key stages of AIOps are:

• Collecting data
• Automatically detecting problems
• Automatically responding to problems
• Learning from past data and analyses

Who uses AIOps?

AIOps is used worldwide by organizations of all types and sizes. These include businesses with large IT setups and companies undergoing digital transformation.

What are the differences between MLOps and AIOps?

MLOps (machine learning operations) is a set of principles for managing machine learning projects in real-world use. It helps keep ML models up to date and bridge gaps between teams. AIOps, on the other hand, automates systems using artificial intelligence and data analysis.

MLOps is a method of standardizing processes, while AIOps focuses on automating activities. That means MLOps provides more clarity to teams, while AIOps analyzes situations independently and even reacts to them.