An amplified term, DevOps is all about assisting developers with being autonomous when it comes to getting their apps to a legitimate environment by performing basic operations. There are various other IT functions that are necessary to keep things performing well that have already been through test or production environment. System administrators and Site reliability engineers (SREs) and specialize in such functions, but are usually puzzled by the amount of data their fields of responsibility create without acknowledging the data created by apps going through their DevOps processes.
This is where AIOps makes its grand appearance in the DevOps space.
What does AIOps do?
Correlation leads to identifying problems in time and resolving them faster. This correlation is achieved by gathering data from all the practical areas and managing them in one place with the help of modern Machine Learning. Machine Learning helps AIOps data platform to execute trend analysis, look for patterns across all the data that is being created, and highlight what is important via web interfaces.
Over time, as the AIOps platform pursues to learn the environment, it can be granted the ability to resolve defined error conditions automatically, as it will be capable of identifying them quicker as compared to human operators.
How does this help DevOps?
When working with data, Machine Learning helps DevOps processes in the following three ways:
1. Summarizing data:
The true power of Machine Learning is that it holds the ability to process data of high volume and variety at much higher accuracy and speed than a human, sorting and indexing the data for later use. A problem that has often occurred because of being knocked out in the summarization may spring up if the data is not summarized to a single data point frequently.
2. Filtering data from their field of expertise:
When we talk about DevOps, developers should be able to find data quickly from different areas in IT. They will find it easy to quickly fetch data from systems that are directly related to their incident, all with the help of AIOps. Be it using correlation GUID (globally unique identifier) that is knowingly put in the logs to support tracking, or using timecodes, AIOps will help developers through the other systems’ data.
3. Knowing its way around:
It is almost impossible for a person to know the most prevalent error messages that might crop up in a DevOps environment. In a model where a business function can be perceived and written in many languages, and also, deployed into production without notifying the Operations team, Machine Learning helps the AIOps platform to recognize quickly what is perceived as normal and starts searching for deviations.
Conclusion
AIOps is quite new in the tech industry. Despite that, it hints at promising prospects for utilization in the industry. Moreover, AIOps is expected to be the next big thing in IT management, according to the experts. AIOps platform will play a crucial role in clearing the way for digital transformations and defeating the complications of the present enterprise IT operations model.
Source: DevOps, Analytics India Magazine, BMC Software
Ajeet Singh
