AI-Ops the future of DevOps
For many years there’s been a constant demand placed on IT professionals and particularly in DevOps to become more proactive and flexible so that the businesses can accommodate and embrace the new challenges quickly to stay competitive. Such a paradigm shift has resulted in more processes by making them automated as a manual intervention that is in demand too slow and cumbersome. This sea change in strategy is commonly referred to as the digitization of IT.
There are extreme shifts which have often raised unanticipated consequences, and those consequences can take up to years to understand. Cloud computing can be one such prime example; it ushered in an era of flexible infrastructure with lower capital requirements. Engineers were, at last, liberated from waiting for deployments as the resources are just an API call away.
AI-Ops is recognized as a shield for using the complex infrastructure management and cloud solution monitoring tools which can aid to automate the data analysis and routine DevOps operations. The original flow of system monitoring tools was not built to meet the demands of the big data as they were unable to deal with the sheer volume of the incoming data and become able to process all the types of data by staying on par with the velocity of the data input.
The cloud monitoring solutions are splitting the data into chunks and separate what is important by cutting off what is seemingly unneeded to operate with the focus teams and statistical instances rather than dealing with the whole integrity of data. The most vital result is that some crucial patterns might be left unseen and can be excluded from the frame of the data visualization phase in data analysis. This is helping to render the whole process as if the big data cannot produce some actionable business insights by providing the value to the data.
AI-Ops entering the Scenario:
It is not humanly possible to process all the incoming machine-generated data at a time. Moreover, these tasks are mainly excelled by the algorithms of AI such as deep learning models. You may wonder how the DevOps engineers put these machine learning tools to good work? In this article, we will enlighten how the AI-Ops can help your IT department by rendering the maximum profit.
A machine learning model is trained to process all the types of data which is generated by your systems, and it is continuing to do so in the future as well. If a new data type is added to the model, it can easily get adjusted and retrains itself by keeping the high performance all-time. This feature ensures that data integrity and fidelity are resulting in a comprehensive analysis and tangible results.
When all the data is analyzed, the hidden patterns are emerging and presents some actionable insights. The DevOps engineer then distinguishes the need for infrastructure adjustments to avoid the performance bottlenecks with the specific data-based suggestions for the infrastructure optimization and operations improvement. When the event patterns are identified, and automated triggers are set; the statistics show that certain events have always led to a particular result as certain actions are needed to be performed to rectify the issues. The DevOps engineers can create triggers and automate the responses to such events.
If a monitoring solution keeps on reporting the increased CPU usage due to the increased number of connection then the Kubernetes can spin up the additional app instances by using the load balancing for distributing the visitor flow and reduce the load. This is the simpler scenario as the real-world use cases can be more complex which allows automating any routine task of DevOps by enabling the machine learning model to launch it under some specific conditions. Hence, it also deals with the preemptive issues without any occurrence of the downtime.
Benefits of AI-Ops:
By deploying the AI-Ops solutions to your enterprise, it helps you to lead a positive end-user experience by providing uninterrupted product availability. The solution helps to solve all of your preemptive problems and removes the data silos along with the root-cause remediation by analyzing all of your data generated instead of working with the stripped-down samples.
It can also help your business to automate all the routine tasks by allowing your employees to concentrate more on improving the infrastructure and processes instead of dealing with the repetitive and time-consuming tasks. The solution provides a better collaboration with the in-depth analysis of the logs by showing the impact of managerial decisions and evaluating the efficiency of the adopted business strategies.
Businesses are required to move away from traditional IT operations by enabling timely problem identification and accessing infrastructure behavior. AI-Ops helps to monitor the behavior at the edge of infrastructure by keeping the cost controls in check and dynamically managing the public cloud utilization.
Future of AI-Ops:
The battle is gearing up as the AI-Ops is transitioning from its infancy by coming up with the more success stories. The bets are being replaced by the VCs and small, or big vendors are bringing new solutions to the businesses. By starting from the log analysis systems a few years back, we are going to see more automated root cause analysis and failure prediction. Intrusion detection systems are learning from unusual traffic to have auto-scaling systems for better prediction.
DevOps is definitely going to replace the traditional IT department as the titles have changed, and new roles are assigned, but the challenges that are faced by the IT departments are designed to address more. This has not only multiplied the scale inherent in microservice architectures, and hence we need systems which are designed for these new challenges. To this, AI-Ops will surely evolve over the upcoming years by enabling the DevOps to embrace the scale and speed of modern development.
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