The IT infrastructure has become remarkably complex; it becomes crucial for IT leaders to create new monitoring processes relevant to their organizations.
IT monitoring covers a wide range of products allowing analysts to determine if the IT team performs at the expected level of service and manage any problems detected. This can be done by basic testing or using advanced tools like machine learning (ML).
As the speed of change in the industry increases, IT operations are required to help the business stay afloat to fill experience gaps and allow customers to focus on their business.
The challenge that the IT monitoring team facing is the tendency to use legacy systems that need to be actively running. This puts the IT monitoring team at a significant disadvantage and leaves them scrutinizing unnecessary noise and missing information packets.
What if the performance of these systems is optimized?
Artificial intelligence (AI) and machine learning (ML) continue to play a vital role in taking the pressure off internal processes.
The road to leverage AI and ML are partly driven by the need to implement data first when building core systems, partly because of the cross-industry leap to cloud.
In such crises as COVID19, companies are trying to capitalize on the power of AI-powered tools, and more organizations are creating pathways that reflect the need for strategic change.
Machine learning in IT monitoring
# 1 | Adjusted alerts
Sharpening the known pain point in traditional anomaly detection systems, using a combination of supervised and unsupervised machine learning algorithms, we can reduce the signal-to-noise ratio of alarms as well as correlate those alerts across multiple toolkits in real-time. Additionally, algorithms can capture corrective behavior to suggest remedial steps when a problem occurs in the future.
# 2 | Comparing the indicators
We can determine correlations between metrics sent from different data sources in our infrastructure and applications through advanced anomaly detection systems based on machine learning algorithms. Additionally, some ML platforms provide one-time cost optimization reports that can compare instance usage to AWS spend.
# 3 | Business Intelligence
Different anomalies can be detected within massive amounts of data to turn them into valuable business insights via real-time analytics and automated irregular detection systems.
Machine learning logic can be applied to metrics obtained from various sources to perform automated anomaly detection before processing the data to mark anomalies that can be scored to be used for identifying how much irregularity the event is.
# 4 | Natural language processing
Machine learning helps define millions of events into a single manageable set of insights using topology, semantic, natural language processing, and clustering algorithms. Similar to the previous solutions, using these algorithms helps reduce the triggered events and alerts, which allow more efficient use of resources and faster problem resolution.
# 5 | Cognitive perception
There is an alternative use of machine learning for IT monitoring to combine ML with crowdsourcing to filter out massive log data to identify events. This helps focus on how humans interact with the data rather than focus solely on mathematical analysis. This approach is called perceptual insights, and it denotes important events that may occur, and that needs to be taken into account.
Although the application of machine learning is not strictly straightforward, its potential is clear to transform IT monitoring. As IT infrastructure continues to grow, it is clear that many industries are turning to ML to find effective and budget-friendly solutions today and in the future.
One side note
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