As we know, IT operations management can be pretty complex. However, there are some relatively straightforward solutions.
And, on that track, Artificial Intelligence has paved its way into the IT world.
As a result, AIOps platforms have come to stay!
Today, we’re highlighting the opportunities AI provides to achieve AIOps insights.
But what is AIOps? Let's find out!
What is AIOps?
The term AIOps emerged in 2016 through a Gartner report and encompasses Big Data and Machine Learning capabilities.
AIOps identifies the value patterns that provide information and support efficient responses.
It use advanced mathematical models, correlation and analysis. Further, AIOps also supports operations like automation, monitoring and service desk.
Last but not least, this approach collects, records and transforms data sources into aversion readable, like graphics or histograms.
To summarize, AIOps platforms use data to collect, present and analyze technology.
Who uses AIOps?
Users who use AIOps are mostly related to the DevOps, cloud computing systems and big data analytics fields.
AIOps are ideal for large companies that need constant monitoring, yet small businesses can also use it.
The main issue for small businesses is the time and money it's needed to maintain these systems.
AIOps also supports extensive data analysis, as seen in Johns Hopkins’s fantastic Covid-19 Maps on GitHub.
How do AIOps work?
AIOps Components
There are some essential components to an AIOps system:
- Data Integration: Reducing data silos allows for easy maintenance and monitoring. It also enables to determine issues' causes and sources.
- Real-Time Processing: Real-time tools are ideal for detecting security anomalies in real time.
- Patterns and Rules: These AIOps patterns allow teams to create specific rules and pattern recognition.
- Domain Algorithms: Algorithms' content and structure are born from the IT data organization of each company.
- AI/ML: AI and ML capabilities integrate mathematical models that synthesize and analyze data to develop reports.
- Automation: Automation is essential to reduce workloads. These processes can integrate new features, log analysis and anomaly detection.
Conclusion
AI for IT Operations is becoming part of our day, and complex IT operations are more and more common.
AIOps gathers enough AI to face the dynamic IT environment and digital transformation challenges and complexities.
Do you want to incorporate AIOps capabilities into your business operations? We can help you!
