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5 Ways AI Will Impact ITOps In 2019

5 Ways AI Will Impact ITOps In 2019

ITOps – 2018 was a good year for enterprise IT with innovative technologies beginning to be adopted by the mainstream. We also began to have discussions about the next leap for IT operations, introducing the idea that artificial intelligence will soon play a major role. 2019 is where this use of AI and machine learning in operations will become more of a reality. Here’s what to expect.

1) Great Complexity Will Demand AI-Driven Solutions  

The need for the support of AI comes from the fact that our IT environments are continuously becoming more complex. Monolithic applications living in a single data center are being replaced with microservices-based applications running in container systems hosted by multiple clouds. And as these services become more ephemeral it becomes impossible for humans to keep track of everything that’s going on. And yet understanding what applications are running where, and how that is impacting the business is critically important.

2) Multiple Tools Will Work Together to Deliver AIOps

Given the heterogeneity and size of modern IT environments, there will be no single tool that gathers the entirety of data available. Instead we will find data from multiple tools being combined into a unified data platform that will then be able to provide a set of powerful tools such as baselining and anomaly detection over massive amounts of metrics and event data. Machine learning will be used to correlate this data together, making problem identification much faster and therefore decreasing time to resolution when problems do occur.

3)  We’ll Move Beyond Root Cause Analysis to Remediation

By combining a unified data platform with domain models that are specific to IT environments, we will begin to not only automatically identify the root cause of any problems, but be able to make suggestions as to how problems can be resolved. Incident response tools will capture the steps taken by operations teams and machine learning will begin to recognize where new events are similar to previous events, elevating the steps taken previously so that they can be quickly re-executed to address the current concern. While human approval will likely be required for most tasks, there may be specific situations where tools go so far as executing remediation steps on their own, similar to today’s auto-scaling features.

This article originally appeared on Forbes.com.  To read the full article, click here.

Nastel Technologies uses machine learning to detect anomalies, behavior and sentiment, accelerate decisions, satisfy customers, innovate continuously.  To answer business-centric questions and provide actionable guidance for decision-makers, Nastel’s AutoPilot® for Analytics fuses:

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