There’s no doubt that the software development and engineering industry has become one of the fastest evolving industries in the world. This greatly affects the way things are done within the industry. The rapid change within the software industry can be said to be in an exponential form. These changes are evolving so quickly that you can measure and track them within daily timelines as compared to the growth of other industries.
These unexpected yet positive changes in the industry are mounting great pressure on all industry key players—developers, IT operations personnel, and even product owners—causing them to readjust and be in sync with the current trend for greater productivity. Companies need to devise means to tackle the ever-growing and complex tasks being thrown at them to create value and remain relevant in the space. That’s where AIOps comes in.
In this article, we will explore the need for AIOps, then move on to understanding the different tools that make up the AIOps toolbox. Finally, we shall consider how AIOps affects the productivity of software firms’ IT operations.
How Did AIOps Come About?
Through IT operations (ITOps), the practice of DevOps solves some of the problems that firms face by introducing collaborative efforts. This became a game-changer for how software firms get things done. This is because ITOps aids in the planning, building, monitoring, and the continuous integration and deployment of new software solutions, or even newer versions of other software.
However, DevOps, as we know it to be now, mainly hinges on the collaborative efforts of the various teams within the firm responsible for ensuring the successful development and subsequent deployment of the software solution. DevOps has been faced with enormous systematic problems that affect the firm’s overall productivity. This led to the creation of AIOps.
Why AIOps?
Artificial intelligence for IT Operations, mostly known as AIOps for short, is a super game changer in the software development field. It involves the integration and application of the tenets of artificial intelligence to the practice of DevOps.
AIOps is the complement DevOps needs to satisfy the ever-growing IT industry. In this section, we will explore why DevOps isn’t enough. Why integrate artificial intelligence into DevOps?
Large Chunk of Data Flow
In recent times, the volume of data being handled by software development firms has tripled in size. The cleaning and sorting out of such a volume of data to glean useful information is an enormous task for DevOps teams. However, with AIOps, teams can easily break down large data and process them.
Repetition of Steps for Every New Task
DevOps teams have to repeat the same process in executing tasks every time they get a new one. Whether it be the building of a newer version of an existing software solution or entirely new software, they’re the same steps and same process all the time. It is boring and burdensome, but AIOps helps with this.
Bug Detection
The issue of detecting bugs found in the code is achieved easily with the introduction of artificial intelligence to DevOps. It also helps in other anomaly detection and the like.
AIOps Toolsets
What distinguishes AIOps are the tools that it has to offer its users. In this section, we’ll explore these toolsets. The basic tools of AIOps are machine learning and big data. AIOps employs machine learning chiefly to execute its tasks. These tasks range from a collection of data, all from diverse sources, as well as the aggregation of the data. It also includes the provision of analytical insights on which firms base strategic decisions.
Another essential part of the AIOps toolbox is the AIOps platform, which aids its ease of usage and application acceptance. To get the best from AIOps, the platform has a great role to play. Let us take a look at what you can expect from such a platform:
- Data gathering capabilities
- Full-stack monitoring and observability
- Great analyzing superpowers
- The provision of insights through data visualization
- The ability to automate most, if not all, of the workflow within the company
- Ease of use. The platform should be very easy to make use of and not too complicated. This will enable more firms to key into the revolution.
Impacts of AIOps on ITOps Productivity
How can AIOps benefit software development firms to increase their productivity and efficiency? The following are the impacts of AIOps on a company’s productivity.
Faster Processing of Data
With machine learning, firms can process data at an amazing speed. This is because the machine learning model has been trained with similar data types, so it is now all about testing the model whenever real-time data is made to pass through the model.
Also, the adoption of AIOps tends to speed up processes from building to deployment. It reduces the time taken to pass vital information across all the teams involved with the project at hand.
Anomaly Detection
When you apply artificial intelligence to something, the AI studies your normal environment extensively. The AI monitors the environment and throws up a warning alert whenever it discovers unfamiliar activity. This allows AIOps to make smarter decisions concerning your products. All this is possible through the capability of machine learning, which gives smart predictions on the flow of things.
Shorter Turnaround Time
AIOps reduces the turnaround time for requests made by end users of the product. This is possible with the integration of professional bots within the application. It tends to understand every on-call schedule and, as such, knows the right people for the task.
With this knowledge, the AI notifies the right people of tasks that require their attention. This simple routine helps mitigate any job duplication or controversy as to who handles what at that given point in time when the task arises.
Failure-Case Detection
The application of AI in IT operations has the capability of simulating how the software solution that firms develop will work, as well as predicting what conditions can trigger a failure in a certain aspect of the software. This helps the DevOps teams know what they should likely watch out for with great attention. For instance, Stackify allows developers to validate the behavior of their code and write cleaner codes.
Monitoring, Collaboration, and Feedback Collecting
The goal of AIOps is to help firms gather feedback from end users. This is because the AI can take in this feedback and sort it out, thereby pointing the teams involved to the most crucial and meaningful information that needs immediate attention. It also enables seamless collaboration between teams working on the same project, regardless of the location of each member of the teams.
This helps firms to keep working and not rely on meeting up in person in order to make decisions related to the work at hand. By implementing AIOps in your company’s IT operations, you can observe and monitor the company’s operation at all times, even when employees are on break or not on duty. For example, teams can monitor and track their activities with Netreo’s suite of tools.
Wrapping Up
Artificial intelligence is here to boost your company’s productivity. Integrating AI with your DevOps and other IT operations shoots your firm’s capabilities up to the moon. To remain relevant in the software development field and create value in the shortest possible time, AIOps is something you should apply to your processes.
As a DevOps engineer, employ the services of great AIOps platforms such as Netreo to discover the wonders of AIOps.
This post was written by Ukpai Ugochi. Ukpai is a full stack JavaScript developer (MEVN), and she contributes to FOSS in her free time. She loves to share knowledge about her transition from marine engineering to software development to encourage people who love software development and don’t know where to begin.