AIOps : Implementing AI and machine learning algorithms in DevOps Automation
Artificial Intelligence, Machine learning and DevOps are some of the most commonly discussed topics in the software development world. And why shouldn’t they be? There are umpteen business benefits in terms of cost savings, innovations and reduced time to market. What’s even better than implementing both of them singularly, is bringing them together. AIOps is catching on quick, and there’s a world of possibilities to explore.
Gartner predicts that by 2022, big data and machine learning functionalities will replace more than 40% legacy services. Decades old infrastructure, thus, can be modernized through AIOps. AI-Augmented automation will also play a larger role in driving IT productivity and ensuring scalability.
An introduction to AIOps
The human mind is quite marvelous. Thinking, real time analysis and quick decision making come naturally to us, which is why human intelligence is a vast area for scientists to explore. Artificial intelligence aims at humanising machines, with smart interactions. It can be defined as a technology that goes beyond routine calculations, through deep levels of analysis that only the human mind can accomplish.
Machine Learning forms the base of the mechanisms through which AI can be implemented. ML essentially makes use of predictive analysis, enhancing the machine’s human understanding and ability to decipher data.
A mix of methodologies, practices and tools that enables organizations to continuously deliver software with agility, simplicity and faster resolution, DevOps has become pretty popular in IT over the last decade. Automation backed, it has proven to speed up operational processes and foster cross functional teams. All in all, work gets done effectively, with less friction, and higher levels of productivity.
Bring both of these together, and the end product is AIOps, a combination of two of the most vastly used tools and philosophies to ensure IT success. Domain based monitoring tools may provide skewed insights, but do not offer a holistic analysis because of disparate data. And with the massive amounts of data generated through the ever increasing proliferation of connected devices, it is important that it With AIOps, streamlining this data and integrating it to identify the root cause of problems becomes simpler. Predictive analytics, service analytics and automated workflows make for better end user experiences, and faster production cycles. Further, this also breaks traditionally constructed silos, allowing more room for innovation.
Use Cases
The best way to understand the implementation of AIOps, is through use-cases. These are some of the use cases where we can implement it.
- In-Depth Log Analysis
In the DevOps lifecycle, lots of data is generated through infrastructure monitoring logs, application logs, performance metrics, etc. With AIOps, machine learning algorithms can be applied to these data sets to predict the problem areas and recommend changes to the development teams. Alert mechanisms can also be triggered to avoid mishaps and system failures
2. Development Efficiency
AIOps also enables data collection related to sprints, user stories, the development velocity for each developer and bugs raised/fixed. So you have everything together, in one place. Machine learning algorithms can analyse all this data together, and perform an analysis for individual developers or team. Preparing and sending daily/weekly/monthly reports to the appropriate audiences based on sprints or releases is also taken care of. Higher efficiency, lower rates of failure and less trouble.
3. AI enabled automated testing
User stories and features can be tested in real time using AIOps. Test cases i.e unit test cases, UAT test cases, regression test or feature tests etc. have traditionally been resource driven, which means they need a lot of time and effort. More decision making needs to happen. AIOps takes over these complex decisions through automation and goes a step beyond what humans can do, while creating alerts for potential problems and allowing developers significant time to work on solutions. All of this happens with minimum human intervention, and you save precious time, money and energy, while delivering superior customer experiences.
4. Software Code Quality
The biggest differentiator for AIOps is its ability to differentiate the sound from amongst the noise. With a DevOps pipeline that can implement quality code and security tools like Sonar, Cast, Owasp Zap, CheckMarx etc., functions like Security, Maintainability, Performance etc. fall into place. AIOps improves software quality as it learns from previous mistakes, which can help CIOs and CTOs be prepared for the road ahead, both from a user experience and cost perspective.
In a constantly evolving tech landscape, it’s important to always stay one step ahead of the competition. Rather, it’s important to benchmark your own success to drive disruptive innovation. A successful AIOps implementation is one of the best opportunities that your organisation can encash upon. An overwhelming 90% of CIOs trust that this approach is the future of IT, and they aren’t wrong. Try it for yourself, and the results will show.
-Mayur Yambal, Team Lead, iauro
