Definition: What Is AIOps?

Artificial Intelligence for IT Operations (AIOps) is a model that automates and enhances IT operations through artificial intelligence (AI), analytics, and machine learning. This is done by leveraging observability data being churned by the various operation tools.

Employing an AIOps strategy gives companies the power of utilizing the data generated by their technology stack to gather insights and discover better ways of running operations. In traditional operations, IT teams look at this data only when performance issues and system outage occur. The AIOps model instead encourages them to be more vigilant since the monitoring happens throughout.

Is AIOps Equivalent to DevOps?

DevOps refers to the continuous development and delivery of a software project with the help of various DevOps automation tools. The model involves following the crucial steps and best practices of gathering information, development, testing, staging, and deployment to production. These stages need to work seamlessly to enable companies to release software products faster, and reduce the risk of downtime of said products and respond to specific DevOps challenges.

DevOps refers to the continuous development and delivery of a project following the important steps of gathering information, development, testing, staging and deployment to production all this in a seamless manner.

AI in IT Operations on the other hand involves all the continuous integration and development processes and adds retraining into the process. This is where the data first ingested to the pipeline keeps training the model through as it learns more and more about the infrastructure, the observability data collected from it, etc. through machine learning.

Therefore, AI Operations differs from DevOps in that in DevOps during the continuous integration and development cycle, the data ingested in the first phase is still the same.

How Does AIOps Work?

AIOps works in three main stages: Big Data, Machine Learning, and Automation.

1. Big Data

An AIOps solution typically uses a Big Data platform to aggregate the commonly siloed data from the various components of your IT environment, networks and applications. The data can include historical performance data, streaming real-time operations events, system logs, and metrics, and network data, to list a few. For example, an ecommerce company might be generating user traffic and purchase history.

2. Machine Learning

Once the data has been aggregated, it is pipelined to train machine learning algorithms that then compile a machine learning model. The pipeline commonly has three main steps:

• Extracting,
• Transforming,
• Loading the data.

For example, website traffic logs might have unnecessary headers that the model would not need for training. In this example, the transformation stage would involve dropping the headers before the data is loaded for model training.

3. Automation

Lastly, once the model is ready, the automation process involves the model in monitoring. During this stage of automation, the pipeline performs tasks such as

• Anomaly detection
• Data mining
• Conducting inferences
• Sending out alerts

Following the website traffic example, the automation stage would identify anomalous headers from the data and send alerts to the IT Operations.

Moreover, automation can also involve having the AIOps strategy include automatic patches to loopholes in the system or rollbacks to a version of the system that is fault tolerant. All of these can be done in real-time. The AI model’s insights, alerts, and recommendations are then relayed on analytic dashboards to improve IT operations.

Benefits: Why Do You Need AIOps?

AIOps helps teams work smarter and faster by saving time that would have been spent sifting through tickets from system failures. More importantly, it can detect issues before they escalate and impact business or end users. An AIOps-centric strategy has many more benefits, most notably:

• Faster Mean Time to Resolution ( MTTR ). Over time as the AI model becomes “smarter,” the predictability of what went wrong and why significantly improves. For example, in the aforementioned web traffic example, the relevant teams would be dealing with data that is already filtered from noisy logs that would not be relevant to downtimes. Thus leading to a faster MTTR.
• Improved collaboration and productivity. Productivity would go up as teams no longer need to waste time sifting through logs to pinpoint the issue. Still on the web traffic example, the AI models would have a proposed solution to the cause of the downtime. Team work would be enhanced through different departments knowing which parts of the system broke on their end from the intelligent filtering of data.
• Reduced costs. Reduced costs come in several forms. One way would be the saved time from incident to response. Downtime costs the business money. Another avenue for saving money would be the less manpower needed alongside an AIOps pipeline. Lastly, the business would save on the potential loss of client revenue due to poor user experience with the system.
• Growth from reactive to proactive to predictive management. Predicting future outcomes gives a company the upper hand in planning and budgeting. The predictive power has the potential to go up as more data is churned. This is due to the constant retraining of the new data. The more of it a company has, the better the AI models become.
• Industry-specific AIOps strategies. Different algorithms vary in performance when subjected to various datasets. As such, features used to train models in finance would significantly differ from those in e-commerce sites. These nuances can be used as an advantage to develop more fine tuned strategies. Furthermore, companies in the same industries can employ AIOps standards that cater to that industry’s needs.

AIOps Use Cases

AIOps drives other important business and IT innovations. These may include:

• Anomaly or threat detection. Using historical data, the AI models learn how to identify anomalies and threats. The model also progressively improves as more detections are made by the Ops team. Consequently, some algorithms are able to identify patterns in data that the Ops team may have overlooked as being an anomaly or threat.
• Event correlation. IT teams are faced with floods of alerts, yet only a handful matter. AIOps can mine those alerts and use inference models to group them and identify upstream root cause issues at the problem’s cause. This transforms the overloaded inbox with alert emails into one or two notifications that matter.
• Intelligent alerting and escalation . After root cause alerts and issues are identified, IT teams can use artificial intelligence to automatically notify subject matter experts of the incident’s location for faster remediation. Artificial intelligence can act as a routing system, immediately setting the remediation workflow in motion before human beings ever get involved.
• Performance monitoring and analysis. The AIOps platform collects logsthat enable teams to perform performance monitoring and analysis on how resources are allocated and used.
• Capacity planning. Using AIOps algorithms that can predict the overall capability of a system, the business is able to plan the human capacity needed to mitigate downtime. Consequently, the business is able to plan the required computational resources for the AIOps platform to operate at full capacity.

How to Get Started with AIOps

The recommended approach to AIOps is to start small and then scale as needed. Starting small means choosing one area of the business that would benefit from AIOps and using it as a test case. Starting with AIOps can be broken down into several actionable steps:

Plan

As part of planning, the following questions can act as the compass for what kind of AIOps process to build:

• What areas of the business would benefit from AIOps?
• What is the preferred effort and time for implementation?
• What would be the cost of maintenance?
• What areas of the business would benefit from AIOps?
• What budget would be allocated for AIOps?

Set goals

Goals are important as they are later used to gauge the success. During this step, the business will need to have actionable goals such as the metrics that will be used to measure impact, performance, time saved or improvements made when handling downtimes.

Identify a test case

Once the business has identified an area that would benefit from AIOps and actionable goals, a test case can be derived. This is important due to the resource-intensive nature of AIOps. Starting with a litmus test would be a precursor for costs and the time taken to migrate the whole system into an AIOps-first model. The test should be done on the system’s most vulnerable and data-intensive cogs. These can be areas of the system that the IT operations team has to keep high monitoring and telemetry on. Areas constantly facing security breach attempts would be a good place to start.

Test different algorithms

A combination of AI and machine learning algorithms can yield more insight into what the IT operations team may be missing. For example, clustering models can reveal data groups that the human mind would struggle to correlate. Other algorithms, such as decision trees, can help automate the correct approach needed to solve downtime instead of trial and error.

Scale

Lastly, once the tests are successfully orchestrated, the team responsible is tasked with explaining the findings to management. The results would inform the tentative time and budget required to scale the AIOps operation in terms of computing resources or more human resources. Scaling can also come as an outsourced AIOps platform.

Choose an AIOps solution

What type of AIOps solution is needed? Is it domain-agnostic or domain-specific? Once the business answers this question, a suitable third party vendor can be considered to provide AIOps as a service.

Monitoring with Sematext Cloud

Sematext Cloud is a full-stack observability platform.

Sematext’s service auto-discovery scans for services and logs that can be monitored by Sematext’s 100+ integrations without additional manual agent installations. With Sematext, you can collect metrics, logs, events, infrastructure and package inventory across your whole stack. It can also monitor APIs, website uptimes, SSL certificates, real user experience, etc. Each integration comes with a number of useful dashboards and reports out of the box, as well as out-of-the-box alert rules.

Sematext allows you to set up alerts and anomaly detection rules, so you get notified by email or one of the many notification hooks like Slack and PagerDuty when one or more predefined conditions in your metrics data are met.

Watch the video below to learn more about what Sematext Monitoring can do for you. Or start the 14-day free trial and test it out yourself.

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