AIOps is a relatively new term. It stands for Artificial intelligence for IT operations, and it can be defined as the process of using artificial intelligence (AI) techniques to maintain IT infrastructure. The rapidly evolving field of artificial intelligence has also reached the automation of key operational tasks, such as performance monitoring, workload planning, and data backup.

If you're interested in artificial intelligence topics, you might also be interested in our article on how Fortune 500 companies harness the potential of AI?
 

Why is AI used in IT operations?

Increasing amounts of data from a growing number of sources make it increasingly difficult to analyze and create a complete picture from individual data points. That's where AIOps comes in.

AIOps platforms aggregate data from multiple sources. They don't distort data by, for example, averaging it from a group, but they have the ability to capture large datasets of any type in the environment while maintaining data fidelity for comprehensive analysis.

At the same time, AIOps has the ability to handle all types of Big Data formats. The platform then applies automated analyses of this data to predict and prevent future issues and identify the causes of existing problems, enabling better decision-making.

Benefits of using AIOps

According to Gartner and Splunk, the five main use cases for AIOps include managing big data, performance analysis, anomaly detection, event correlation, and IT service management.
 

In the context of performance analysis, AIOps is a key use case. Artificial intelligence and machine learning can be deployed to rapidly collect and analyze vast amounts of event data to identify the root causes of issues. As mentioned earlier, performance analysis, a critical IT function, has become more complex as the volume and types of data have increased. AIOps helps solve this problem by employing more advanced techniques. It can also predict likely issues and quickly conduct root cause analysis.

AIOps aids in anomaly detection, identifying events and activities in a dataset that significantly deviate from historical data. It relies on algorithms such as trend tracking algorithms, monitoring a single key performance indicator (KPI) and comparing its current behavior to the past. If the result anomalously increases, the algorithm generates an alert. The coherence algorithm analyzes a group of KPIs that should behave similarly and generates alerts if the behavior of one or more of them changes.

The third major use case for AIOps is event correlation and analysis. It can be defined as the ability to take a broader view through a "storm of events" from many related alerts to the actual cause of events. This approach facilitates decision-making regarding remediation. AIOps uses artificial intelligence algorithms to automatically group relevant events based on their similarity. This reduces the burden on IT teams in continuous event management and reduces unnecessary event noise and traffic.

Through AIOps, we can also manage IT services. AIOps brings benefits to ITSM by applying artificial intelligence to data to identify issues in cloud, containers, servers, networks, etc. AIOps monitors infrastructure performance and automates issue resolution.

AIOps deployment examples

AIOps can help address IT operations challenges in various industries. Splunk provides several interesting examples:
 

Healthcare:
 

• Reducing risks associated with mobile networks and the practice of healthcare workers bringing their own devices.
• Preventing ransomware attacks that disproportionately target medical organizations.
• Enabling access to large datasets, both internal and external, for research and diagnostic purposes.
   

Manufacturing:
 

• Automating the collection and analysis of diverse data sources generated by supply chain integration, factory operations, and product and service lifecycle management.
• Using real-time monitoring to track every machine on the production floor.
• Preventing production slowdowns and eliminating defects using historical data combined with AI-based predictive analysis.
• Using machine data to enable predictive maintenance, repairing machines before they fail.
• Better leveraging data to create more efficient supply chain management systems.
   

Financial services:
 

• Preventing increasingly sophisticated security breaches and cybercrime.
• Sharing customer data for marketing purposes.
• Analyzing historical customer data to create more accurate revenue growth forecasts.
• Ensuring data security and regulatory compliance.
• Providing a framework for integrating multiple large datasets to enable the adoption of new technologies such as blockchain.
• Keeping pace with consumer expectations for mobile and digital banking experiences.
• Improving network speed and performance.
   

AIOps - where to start?

As a global leader in cloud solutions, Amazon Web Services (AWS) offers a range of AI and machine learning-based products to help kickstart AIOps implementation. You can use them to improve customer experiences, streamline business service delivery, and reduce costs. These include:
 

• Amazon DevOps Guru, a machine learning-based service that helps development teams automatically detect abnormal operations in the cloud.
• Amazon CodeGuru Security, a software testing tool that automatically scans and identifies code vulnerabilities using machine learning algorithms. 
• Amazon Lookout for Metrics automates anomaly detection and performance monitoring across various AWS workloads and cloud applications from other providers.

As a longtime AWS partner, Hawatel has special expertise in implementing AWS products. If you'd like to discuss AIOps or have any other questions, feel free to reach out to us!