Three elements for successfully building and deploying AIOps

Today, as big data proliferates in every aspect of business, IT teams are faced with the daunting task of handling the sheer volume and complexity of operations. As a result, enterprise demand for AIOps is growing.

AIOps (artificial intelligence IT operations) uses big data and machine learning (ML) to predict and identify at a scale and speed that humans cannot achieve , diagnose and solve IT problems.

A recent report from private equity and venture capital firm Insight Partners estimates that the AIOps platform market size will increase at a compound annual growth rate of 32.2% from 2021 to 2028, from approximately $2.83 billion in 2021 Growth to $199.3 billion in 2028. That said, effective AIOps solutions don’t happen overnight.

A complete AIOps solution comes from a solution that has been refined over a long period of time and contains three basic ingredients: data, analysis and expertise in different fields.

Data

Without data, successful AIOps does not exist, and this part is crucial. While data is in ample supply, the challenge is getting it in a usable and reliable form. AIOps relies on hundreds or even thousands of data points from disparate sources such as network performance, business systems, and customer support, all generated on a second-by-second basis, and in many cases at sub-second rates. How you handle large amounts of data can make or break an AIOps solution. Separate pipelines for on-device and off-device data management yield the best results in terms of speed, cost-effectiveness, and maximum efficiency.

The traditional single internal data processing model can no longer cope with the complexity and volume of today’s data sets. Instead, consider building or re-architecting your data processing funnel into two parts: a lean, fast processing pipeline that handles time-critical analysis via a real-time, on-premises data bus, and a more robust pipeline that handles time-critical analysis in the cloud. the remaining data. Reduce on-premises data production to a minimum and allocate the cloud (equipped with elastic computing and more sophisticated storage capabilities) to process the remaining data, enabling faster and more cost-effective data synthesis.

A decoupled pipeline model that manages both internal and external data can enhance an organization’s ability to process millions of data points per hour. Machine learning (ML) algorithms can help prioritize incoming data from each pipeline and transform raw, unstructured data into usable metrics critical to customer service or IT operations teams. The efficiency and speed gained from the two-pronged system also enables organizations to deploy enhanced monitoring capabilities to gain real-time visibility and long-term trend information on network performance.

Analysis

The second key factor to AIOps success is analytics. Analysis in AIOps is divided into two stages, including exploratory analysis (screening out trends or anomalies from raw data that require additional inspection) and advanced statistical analysis (translated into actionable insights). While exploratory research plays an integral role, engineering teams are often eager to jump to advanced statistical analysis as data flows through the pipeline. Bypassing this initial stage can lead to data bias—injecting bias into the AIOps process and incorrectly identifying problems, rendering AI/ML algorithms useless and leading to undesirable operational consequences.

Exploratory analytics rely on ML and data scientists to identify and determine the specific metrics that matter. In the process, IT teams may gravitate toward ML—an exciting and efficient technology. But pure ML is not always the most effective method of analysis. ML attempts to solve a specific problem based on a specific set of parameters. Engineers write ML algorithms based on the metrics they believe are needed to reach conclusion A, B, or C—thus ruling out other possible solutions or statistics.

In contrast, statisticians and data scientists examine raw data not with specific results in mind, but rather with examining the data for patterns or anomalies. Manual data review is tedious, but experts can identify immediate IT solutions without the need for advanced statistical analysis.

When the team is confident that the trends or anomalies identified during the exploration phase are correct, they can move on to advanced statistical analysis and training AI/ML algorithms. Even AI/ML requires trial and error testing and does not produce immediate results. Behind every AIOps solution is a team of domain experts who extensively tune and test AI/ML models to ensure AIOps success.

Different areas of expertise

The third element for successful AIOps implementation is domain expertise. In the creation of AIOps, there isn't a lot of experience to draw from. The successful deployment of AI in any enterprise requires the involvement of experts in different fields. For example, in the field of network operations, network engineers understand the nuances of ML systems and the necessary AI algorithms to accurately solve specific problems. At the same time, non-technical experts bring industry-specific knowledge such as the provenance and availability of data sets, business strategies and operations. A large number of domain experts ensure that AI/ML algorithms reflect real-world operations, provide critical verification of results, and serve as an important tool to check for faulty methods or unintended consequences. For example, a communications system undergoing planned maintenance may exhibit behavior that typically indicates a problem state (such as extremely low network traffic). Adding a business logic layer to the model predictions that communicates with the maintenance ticketing system can eliminate these false alarms.

Domain experts play an important role in explaining to an audience of executives hungry for AIOps solutions. ML tends to operate in a black box, leaving teams unable to clearly illustrate how the model made a specific decision. This can lead to skepticism and hesitancy among business executives about AI-driven insights and actions. On the other hand, explainable artificial intelligence can gain stronger recognition and trust from business leaders who are unfamiliar with AIOps.

AIOps requires three core ingredients, but, like any recipe, the quality of those ingredients, and whose hands they are placed in, will determine the final outcome. Trial and error is part of the innovation process, especially in the complex art of training ML. Ensuring that data is handled correctly, using the right types of analytics and engaging domain experts will help enterprises deliver successful, scalable AIOps solutions that meet the growing demands for operational efficiency.

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