Posted by Priyansha Sinha | Last Updated: 14-Sep-18
The cloud solution monitoring tools and complex infrastructure management have formulated an umbrella term for automating the DevOps operations and data analysis- predominantly known as AIOps.
The system monitoring tools that were built around 10 years back had major flaws mainly because they were not devised to work on the core concepts of Big Data. They were also not able to process all the possible varieties of data types, unable to deal with a large amount of incoming data, or stay on par with the data input velocity. As a rule of thumb, these cloud monitoring solutions are expected to break the data into pieces, cut off what is supposedly not required, separate what is seemingly needed the most, and process with statistical samples and focus groups instead of operating with the whole integrity of data.
AIOps Joins The Scene
Processing each diversity of the incoming machine-generated data on the exact prescribed time and situation is not humanly possible, obviously. However, this is precisely the kind of tasks Artificial Intelligence (AI) and its algorithms such as Deep Learning models excel at. The only crucial question remaining is this: how to implement these Machine learning tools into the lives of DevOps engineers?
Here is how AIOps can greatly assist your IT department:
Faster Processing Of ALL The Data
A machine learning model can be devised to process every kind of data generated by the systems- and it will undoubtedly do so in not-so-distant future. If a new variety of data gets added- a model can significantly keep its performance at an all-time high and easily adjust & restraint to the upcoming challenges. This will ensure data fidelity and integrity, leading to tangible results and comprehensive analysis.
In-depth Data Analysis
When all the available data is analyzed, the hidden patterns lurk out and the actionable insights deliver themselves. The DevOps engineers can, therefore, tell apart the requirement for infrastructure adjustments so as to prevent any performance bottlenecks.
The Routine Tasks’ Automation
The moment event patterns are distinguished, the automated triggers can then come to action. Thus said, the DevOps engineers can generate the triggers and completely automate the final responses to such events whenever the statistics notify for the rectification of an issue.
The Business Advantages Of Using AIOps
Deploying the AIOps solutions in your business and overall services can enable you to achieve the following benefits and outcomes:
- Automation of routine tasks
- Better collaboration
- Preemptive problem solving
- Root-cause remediation
- Removal of data silos
- Uninterrupted product availability
A Final Note On AIOps And Why Is It So Important
As you can gather from my points, AIOps solutions and tools can be highly beneficial and advantageous for the overall growth of your business. Many companies and enterprises are already amalgamating their efforts to include machine learnings models, AI algorithms, and DevOps system to offer cutting-edge infrastructure automation and cloud monitoring services for tomorrow.
What are your thoughts on the implementation of AIOps services in order to enhance the customer experience? Let us know by jotting down your ideas in the comment section below.