AIOps is an umbrella term for consuming difficult infrastructure management and cloud solution monitoring tools to computerize data analysis and repetitive DevOps operations.
The main fault of system monitoring tools constructed 10 or even 5 years ago is that they were not built to encounter the demands of the 3 V’s of Big Data. They also cannot contract with the sheer capacity of the incoming data, be able to procedure all the diversity of the data types, or break on par with the velocity of the data input. As a law of thumb, such cloud monitoring answers have to divide the data into chunks, distinct what is outwardly important and cut off what is seemingly unnecessary, operating with emphasis groups and statistical examples in its place of dealing with the entire integrity of data.
AIOps Enters the Scene
Handling all the incoming machine-generated data on time is not physically imaginable, of course. Though, this is precisely the sort of tasks Artificial Intelligence (AI) algorithms like Deep Learning replicas excel at. The only outstanding question is the following: how to place these Machine Learning (ML) tools to decent work in the everyday life of DevOps engineers?
Here is in what way AIOps can help your IT department:
Process ALL the data rapidly. An ML model can be trained to process all types of data generated by your systems and it will do so in the future. If an original type of data must be added a model can be moderately easily adjusted and retrained, keeping the presentation all-time high. This will guarantee data honesty and loyalty, resulting in a complete analysis and tangible results.
Suggested read: Most popular AI models
In-depth data analysis. When all the data is examined, the hidden designs emerge and actionable visions present themselves. The DevOps engineers are able to differentiate the need for infrastructure changes in order to avoid the presentation bottlenecks and can have a base at the C-suite table with precise data-based suggestions for infrastructure optimization and operations improvement.
Automation of routine tasks. When the result patterns are recognized, automatic triggers can be set. Thus said, when the statistics show that positive events always main to a particular (negative) consequence and sure movements must be performed to correct the matter, DevOps engineers are able to make the triggers and automate the responses to such events.
Business Benefits of Using AIOps
Deploying AIOps solutions allows achieving the following positive outcomes:
- Uninterrupted product handiness, foremost to a positive end-user experience
- Preemptive problem solving, instead of permanent firefighting
- Elimination of data silos and root-cause remediation, due to the examination of all the data your business makes instead of working with exposed down samples
- Automation of routine tasks, permitting your IT department to essence on improving the infrastructure and processes, in its place of selling with repetitive and time-consuming tasks
- Better partnership, as the in-depth analysis of the logs, supports display the influence of managerial choices and evaluate the competence of adopted business strategies
Also read: Automation and advantages of DevOps
Ending Thoughts on What AIOps Is and Why It Is Important
As you can see, choosing AIOps tools and solutions can be importantly useful for your business. This strength seems to be a marketing trick of AIOps solution vendors, but there is nobody as of yet. The late popular of businesses are stressed with their transition to DevOps culture and performing their digital transformation.
At the similar time, the truthfully innovative companies are already smearing their struggles to joining AI algorithms, ML models, and DevOps systems to transport the cutting-edge cloud monitoring and organization automation solutions of tomorrow.
However, even these innovators do not have an out-of-the-box solution available for their needs and have to build such systems themselves using popular DevOps tools like Splunk, Sumologic, Datadog, Prometheus+Grafana, Kubernetes and Terraform, etc.