What is AIOps? – AI for IT operations explained
Every business now depends on IT. Efficient IT Operations is mandatory for all businesses, especially those operating in a hybrid mode – a mix of existing data centers and multi-cloud locations. As with any business process, IT operations can be augmented with machine learning-based solutions. IT is particularly fertile ground for AI as it is mostly digital, has seemingly endless processes requiring automation and there are gigantic amounts of data to process.
IT Operations are expensive!
According to Research and Markets data, global IT Ops & service management (ITSM) is predicted to reach $35.98 billion by 2025 with an annual growth rate of 7.5% YoY. As the importance of IT operations has ramped up, so has the pressure on ITOps teams. A range of issues puts pressure on teams: shrinking budget for IT operations, multi cloud-based applications, dynamic scaling of infrastructure, limited availability of experienced ITOps personnel, the constant threat from outsiders given the nature of cloud applications, the extension of applications to edge locations with IoT and mobile devices. AIOps is here to support the maintenance teams and provide AIOps tools to solve problems once thought unsolvable.
What is AIOps?
AIOps supports infrastructure management and operations with AI-based solutions. It is employed mainly to automate tasks, improve process efficiency, quicken reactions — sometimes even to a real-time response rate — and deliver accurate predictions on upcoming events. The big data revolution and machine learning technology have driven change, making it possible to process the vast amounts of information IT infrastructure generates. AI can solve the following challenges:
- Anomaly detection – despite fluctuations and the dynamic nature of data, the internal infrastructure ecosystem is a stable environment. Thus, any anomaly can signal the existence of a problem. Also, early detection of an anomaly is usually a sign of a problem that has yet to be fully understood.
- Event consolidation – An AI model can simplify huge amounts of data, dividing it into multiple layers and finding insights.
- Service tickets analytics – when fed data on tickets submitted to a service desk, an ML-based model can predict seasonal spikes and requests. This can help the service desk owner deploy help desk personnel s needed.
- Detecting seasonality and trends – when using an AI-powered solution, any trend can be divided into 3 components – seasonality, trend and residual. That increases the predictability of long-term commitments and makes managing them more effective.
- Frequent pattern mining – machine-powered analysis delivers insights that are beyond the reach of humans. Machines not only process more data but also , unlike humans, make unbiased decisions. They also find correlations that are impossible for humans to detect.
- Time series forecasting – AI-based models can forecast future values such as memory load, network throughput ticket count or other values in the future. This enables AIOps solutions to deliver early alert predictions.
- Noise reduction – AIOps solutions eliminate noise and concentrate on the real underlying problems.
AI helps ITOps run smoother
There are currently several major challenges for IT departments.
According to IBM data quoted by CSO, the average time to identify a breach in 2019 was 209 days. Such a sizable delay is caused mainly by security teams being overwhelmed with work and the stealth operations of criminals. Cybercrime is a highly profitable venture, with profits reaching up to $1.5 trillion a year. Cybercriminals don’t play favorites, targeting victims of all stripes, from individuals to international corporations. In May 2019 authorities tracked down a group that had availed itself of an estimated $100 million. Anomaly detecting AI and machine learning-based AIOps solutions can spot even the slightest signs that unexpected events are occurring in a system. AIOps can be trained to learn what “typical operations” look like and spot anything out of place. It can also send real-time notifications to the team.
According to an ITIC study, for 86% of companies surveyed, a single hour of downtime costs $300,000. For 34%, the cost comes in at a staggering $1 million. AIOps comes with various tools to help keep the lights on and operations running smoothly. In addition to anomaly detection, time series prediction serves as a benchmark and a tool for designing maintenance flow. It also supports efficient resource management. Pattern mining spots inefficient components and bottlenecks to be optimized. It also enables the mapping of both seasonality and trends, so resources supporting operations can be assigned efficiently.
Before the cloud, companies were forced to overpay for servers and computing power because they had to stay on top of fluctuations in their seasonal needs for computer power. Today, despite the access they have to endless power and storage in the cloud, IT teams around the world continue to struggle with capacity planning and delivering scalable infrastructure to meet the irregular demands for infrastructure. Nearly all AIOps functionalities support the goal of delivering a stable and scalable environment. With capacity planning supported by time series forecasting and ticket analysis, IT teams can manage their infrastructure scaling and maintenance not only to avoid downtime but also to minimize costs and utilize their systems as efficiently as possible. A great example comes from Google, whose AI-based system delivered new operational efficiency recommendations for data center cooling systems, effectively cutting costs by 40%. Noise reduction and pattern mining deliver clear insights. On the other hand, scraping through the data in real-time enables an AIOps platform to deliver insights faster and using those insights more actionably.
AIOps machine learning-powered solutions can significantly improve today’s data-heavy, cloud-native IT infrastructure management.
Also, check out my recent blog post I wrote in Forbes differentiating between AIOps, Observability, and Monitoring: “AIOps vs Observability vs Monitoring – What Is The Difference? Are You Using The Right One For Your Enterprise?”
As ever, if all this is still too confusing, please reach out to me. I would be more than happy to help in any way I can with your “fully observable, AIOps-infused cloud-native” journey!