IT teams continue to explore machine learning as a way to drive efficiency and increase productivity within their day-to-day roles. And admins don't necessarily need to be data scientists to realize the benefits of AI in their workflows.
Automated root cause analysis
Changes occur frequently on IT systems, making it difficult to determine the root cause of an issue when one occurs. This challenge is compounded in complex IT environments that span on premises and the cloud.
IT operations teams can use machine learning-enabled monitoring tools, along with time-series data, to more quickly identify the culprit of an issue. For example, if a particular system suddenly starts to use a high amount of CPU, an AIOps tool could use machine learning to trace that issue back to a likely cause.
There are various machine learning use cases in IT operations that apply to the help desk. One of the more prominent examples is chatbots.
Many enterprises already implement chatbots -- which use a technology called natural language processing to receive and reply to questions from humans -- as the first line of defense for help desk operations. Chatbots enable fast response times, as they link back-end data and documentation to text input from the end user. They can also free up valuable time for IT help desk staff.
Powered by supervised machine learning algorithms, sentiment analysis enables IT operations teams to better assess end-user satisfaction.
Admins can apply sentiment analysis to large volumes of data, or text, to determine a user base's overall feeling toward the IT services and support they receive. For example, IT operations teams can use sentiment analysis on user surveys related to incident response to determine satisfaction levels and identify potential areas for improvements.
Many IT platforms gather large amounts of data related to the processes and events that occur on enterprise servers and devices. Patterns in this data can shape predictive machine learning models that help IT teams forecast future events and issues. For example, IT teams could apply predictive modeling to anticipate -- and prepare for -- a specific incident on end users' machines.
There's a big push in enterprise IT for tools such as DataRobot and H2O, which run autoML workloads to find the best possible machine learning model for a data set. For IT admins, this reduces the complexity of building predictive models.
A popular KPI for IT services is the mean time to recovery (MTTR) -- the time it takes to resolve an incident. It is one of the most critical help desk metrics, as the longer an issue takes to resolve, the more frustrated -- and less productive -- an end user will be.
Another machine learning use case in IT operations is reduced MTTR. For instance, an end user calls the help desk and complains about receiving a blue screen of death. A machine learning model evaluates that user's device data and finds the likely cause is associated with a recent Windows update. This helps the technician get to the root of the issue, and therefore solve the user's issue more quickly.