Applying Machine Learning algorithms and Big Data predictive analytics to DevOps workflows can value add extra worth, use resources more proficiently, and rationalize software delivery pipelines.
DevOps cannot be defined as a one-time brave push for digital transformation. Instead, it is a long-term promise to analyzing software delivery and infrastructure bottlenecks in order to change the constant firefighting with timely elimination of the problems’ root causes. Machine data like records and metrics from multiple IT infrastructure monitoring tools permit us to keep a hand on the pulse of the position quo of the IT systems and respond to issues rapidly.
Predictive Analytics in DevOps Workflows
This is where modern monitoring tools like Sumo Logic, as well as custom-built monitoring solutions, come to the DevOps team’s help. For example, Sumo Logic can both aggregate all the logs from a variety of your IT systems and use the Log Reduce feature to sift through the lake of dependent data to discover inconsistencies and anomalies, highlighting possible questions. Combining this with time graphs helps to picture patterns and better define both “normal” system behavior and peak load or other points of importance.
Apart from frequently self-explanatory practice of the predictive analytics in DevOps production environment monitoring, there are equally a portion of other use cases, like application delivery tracking, and using DevOps tools like Git, Jira, Ansible, and Puppet to monitor the flow of the delivery process and expose anomalies and patterns in it. DevOps engineers can recognize unexpectedly massive volumes of code or continued build times, little release speed, and any other bottlenecks or new in the software delivery workflows.
Application quality enforcement. Once the testing tools deliver the output of the next testing run, ML algorithms can detect new errors, alert the testers of the case, and sometimes even compose a test design library to hurry up the process of fixing those bugs. Such method greatly increases the competence of testing, resulting in advanced application quality and smaller time to market.
Suggested read: The rise of DevOps engineers in the current market
Application delivery security. Usage patterns are effectively our digital fingerprints. Studying the standard activity of legitimate DevOps engineers helps make models of suitable behavior. ML models can then detect anomalies and predict potentially malicious use, thus helping to stop possible security breaches on the go. This obviously results in the mitigation of millions in potential damage.
Application performance in production. The similar goes for the app’s regular performance patterns. Noticing any fluctuations clues to automatic provisioning of additional resources during the peak loads, or eliminating extreme ones during idle periods. This also applies to detecting the beginning of DDoS attacks or issues like memory leaks.
Read: DevOps key practices
Reduction of alert storm floods. Observing a plethora of systems and apps in production typically results in real alert storms. While some of these attentive messages are vital, filtering them out of the stream is quite a difficult task. However, such logging helps establish the patterns that lead to issues and highlight the very first alerts for each malfunction. After that, the usual alerts can be abandoned, so only the crucial messages are accelerated to the DevOps teams. This is one of the most valuable applications of predictive analytics in DevOps.
Production failure prevention. Alternative important benefit stems from the earlier point: the ability to avoid major production failures by replying to early triggers. This helps build streamlined workflows that enable resilient IT infrastructures operating with the topmost efficiency. Escaping problems instead of fighting the significances can help save a lot of money, effort, and time.
Also read: DevOps model and practices
Final Thoughts on Using Predictive Analytics in DevOps Workflows
As you can see, imbuing DevOps workflows with predictive analytics provides immense benefits for many aspects of the software delivery lifecycle. From decreasing waste in software development entirely the way up to finish DDoS attacks and applying minimal TTR (time to recover) from key disappointments, implementing predictive analytics is an vital step for any company that aims to use DevOps services proficiently.