In the last few months, anyone interested in AI has probably come across the term Artificial Intelligence for IT Operations or AIOps. The AI ML Development company must take that next step in their digital transformation journey. AIOps is not an all-around solution for all the diseases that plague ITOps or enterprises. However, it is an excellent tool that solves several problems while enabling agility and delivering the best results. It can help IT companies meet the market’s demands in real-time, which is essential in today’s world.
In truth, AIOps resolves four distinct issues currently causing several IT companies discomfort or will in the future.
The four sections are:
- Determine the Business Service Impact of IT Problems
- Eliminate Application Blind Spots
- Use of Accurate Configuration Management Data (CMDB) to automate incidents
- Tame Cloud Sprawl
In this post, we will discuss AIOps and their practical use cases in detail. So, keep reading
AI ML Development Company – What is AIOps?
Before going any further, let’s first discuss what AIOps contains and does not.
AIOps is a framework that uses big data and machine learning to facilitate automation. Data is a crucial part of the definition here, and it is because this factor determines whether the AIOps work well or not. You can apply any advanced neural net, but your data will decide whether it is successful. Hence, most of a data scientist’s time goes into collecting and preparing the data.
Data scientists spend so much time in data collection and preparation because the current data lake they get needs to be better maintained. They receive data from several sources in the IT ecosystem. However, all it is gets pooled, and it can lead to duplication. Moreover, there is misalignment and misclassification of data. Data scientists spend more time untangling the information than drawing actionable conclusions. Ultimately, it costs the company more time and time.
The artificial intelligence consulting services can offer AIOps solutions to help your business leverage the technology to better your operations.
Use Cases of AIOps
Companies need an effective game plan for organizing and synthesizing data. It will ensure clean, consistent, complete, and efficiently grouped data enabling data scientists to spend more time on actual work rather than data quality. Here is how we are leveraging data to solve this issue:
Eliminate Business Service Impact
Every enterprise wants detailed information on how its services perform in the market. And for a company to create effective strategies, they need more than a vague ‘working or not’ answer. Firstly, they must determine how well their services perform, including their health, availability, and IT and business services risks. While this information is necessary for companies, acquiring it is extremely challenging and costly. It requires a variety of point tools with its data model. The companies must understand the relationship between each aspect of the business service to understand the health of key services and not get the ‘noise’ of individual device metrics.
Eliminate Application Blind Spots
Application outages are generally missed by the APM tools. Responsible for around 5 to 10 percent of apps in service, the legacy tools can leave users with blind spots, even though they cost $200 or more per server per month. To remove application blind spots, you need complete insight and understanding of every application element and what infrastructure it uses, along with context to understand the collaboration of every component. The ability to map your applications to the infrastructure will allow you to get all the necessary insight to get rid of any blind spots.
Use of Accurate Configuration Management Database (CMDB) for Automating Incidents
CMDB is the tool that is necessary for maintaining configuration item inventory for relationships. For many, it is an annoying necessity. CMDB only works if you can offer accurate and timely information, making it challenging to maintain, especially in an ecosystem where virtual technology can come into existence and disappear at a moment’s notice. On the other hand, inaccurate CMDB means ineffective automation, resulting in the continuation of long and tedious manual processes and loss of investment.
Taming Cloud Sprawl
For many organizations, the efficiencies of the cloud diminish due to the chances for sprawl and an increase in cost.
Per EMA research,
- 35 percent of enterprises are currently using 4 or more public cloud providers.
- 72 percent of enterprises are currently struggling due to unsanctioned and, most times, ungoverned Kubernetes environments.
While the need for traditional infrastructure visibility is not new, the loss of insights due to cloud consumption only exacerbates the problem. It leads to more use of cloud resources, costing more money, time, and productivity.
How Do AIOps Help Solve These Issues
Now that we have discussed the issues that organizations worldwide struggle with let’s learn how AIOps solve them.
AIOps uses the algorithmic analysis of IT data and observability telemetry to help IT Ops, SRE teams, and DevOps work faster and smarter. You can hire an AI ML Development Company to leverage the service, detect the digital-services issues quickly, and resolve them before they impact the business operations or customers.
Also, AIOps help manages enormous and complex data that companies receive through modern IT environments, leading to fewer outages, better uptime, and continuous service assurance.
Conclusion
The right AI ML Development company will help you create AIOps solutions that allow organizations to manage the speed expected of modern business while ensuring exceptional user experience through AI. So, start leveraging AIOps to better your business operations with MoogleLabs. We will help you find the best solutions for your every need.