AI is one such technology that can be applied to every business process, as it runs on data. With the advent of internet and computing power, humans have created troves of data in past few decades. The rapid adoption of DevOps practices has created similar amounts of data within the companies to be utilized for creating intelligent AI systems particularly using Machine Learning (ML) accentuating their DevOps practices.
Is it worth investing in Machine Learning and Artificial Intelligence for DevOps efficiency?
Machine Learning has been one field getting a lot of traction after technology giants like Google made frameworks like Tensorflow open-source. It is very clear that the future of application development is about intelligent systems, utilizing data being created and letting the systems learn by their own. This is a new paradigm shift and whether companies are ready or not, it is going to take over.
There have been, over the past 5 years, companies have invested a lot of resources to collect data to build Machine learning algorithms for their specific use cases. So, does Machine learning or AI make sense in the DevOps world? How does it help improve efficiency? What use cases have been explored successfully in this space already? And for answers to many more questions, read on!
Data is the King
Anyone who has explored ML/AI knows that Data is the King. Without a huge dataset, it is difficult to derive feature sets and achieve high levels of accuracy for any Machine learning algorithm. If you look around, your DevOps toolchain is already producing a huge stream of data, as your developers work through bug fixes and releases. The stream of data related to your git commits, milestones and releases, infrastructure deployments, test execution, build logs, application log files, the list just goes on and on. Many companies have been successful in leveraging this data stream and build algorithms that resulted in improved efficiency.
Invest in data
DevOps data not only helps technical teams but also helps business teams in understanding how DevOps is improving their bottom lines. This helps businesses in achieving their goals, schedule releases and predict customer satisfaction and take immediate corrective actions. DevOps with data helps in providing end to end visibility on the software development lifecycle keeping everyone informed in the loop. DevOps data is the first point to create automated and intelligent systems that helps in developing advanced tests for complex and distributed applications.
Companies are now utilizing DevOps data to create automate and intelligent systems that helps in creating tests and executing them. They produce data that are utilized to create automated for complex and distributed applications that are being developed.
Leveraging the power of Data in DevOps
Machine learning has found the right fit in anomaly detection and prevention for a long time now. Companies have successfully blocked attempts to hack a network by identifying patterns and detecting anomalies. Going by the same principles many of the real-time application and infrastructure monitoring tools, which provided analytics and dashboards have integrated Machine learning as one of the key capabilities, to help predict application or infrastructure failures, and notifying appropriate stakeholders to take corrective action. Splunk, Elasticsearch are few examples in this space. Similarly, many companies are now beginning to look at patterns in their planning tools such as Jira, to improve their planning efficiency. Many other use cases related to code commits, build failures are being explored to increase the overall DevOps efficiency.
Going beyond Descriptive and Predictive analytics
As we have seen, many of the use cases have been more around Descriptive Analytics, i.e., providing inferences automatically based on what has occurred, and Predictive analytics, i.e., identifying an error or event before it occurs. These two approaches themselves provide huge efficiency improvements for Operations and Business teams, but the real power of AI is when issues can be predicted and can either be remediated automatically or solutions prescribed which point to Knowledgebase articles, for a quick fix. This approach also is known as Prescriptive analytics, has the power to drive DevOps in a much more efficient and effective way.
Continuous feedback as an outcome of Prescriptive analytics
Companies have discussed it, some have even claimed their systems provide continuous feedback from customers, but the true continuous feedback loop involves not just getting feedback from end users but being able to provide faster feedback at each stage of CI/CD pipeline. Prescriptive analytics is already being applied by companies like Atlassian in one of their most popular support tools, Freshdesk. Their machine learning algorithms drive huge business efficiency, due to its ability to quickly understand and respond to end users with prescriptions to their problems. Being able to prescribe a solution across stakeholders in DevOps lifecycle, and analyzing the usage patterns will enable true continuous feedback across the lifecycle.
Driving efficiency true DevOps way
As discussed above, and with details of how some companies are already driving business and operational efficiency, it will only get better with time. The automated process of identifying patterns and suggesting corrective measures will bring out the true power of DevOps. The future businesses that are trying to drive efficient DevOps practices using AI, we will see more faster deliveries, less failure rates, improved customer satisfaction ratio and seamless experience on trillions of connected devices.
Is it for me?
It depends on how efficient you would want your processes to be. If you are just getting started on the journey, why not leverage tools that have the intelligence built in? If you have achieved a good level of maturity and efficiency in your DevOps implementation, it is time your data did the work for you, and for those who are in midst of their implementation, or have some level of DevOps maturity, pick your use cases.
Companies starting to put efforts in utilizing data for DevOps efficiency, should look to invest in capturing engineering life cycle data. DevOps data helps in aligning technological initiatives with business goals. Now is the right time to get started!