Applying Machine Learning algorithms and Big Data predictive analytics in DevOps workflows can help add more value, use the resources more efficiently and streamline the software delivery pipelines.
DevOps cannot be described as a one-time heroic push for digital transformation. Instead, it is a long-term commitment to analyzing the software delivery and infrastructure bottlenecks in order to replace the constant firefighting with timely removing the problem root causes. Enrolling for DevOps Certification can equip professionals with the necessary skills to excel in this commitment. Machine data like logs and metrics from multiple IT infrastructure monitoring tools allow keeping the hand on the pulse of the status quo of the IT systems and respond to the issues rapidly.ing it imperative for professionals to consider an Artificial Intelligence Course to stay updated with the latest trends and skills in this dynamic field.
However, an ounce of treatment is worth a gallon of cure, you know. Dealing with the issues before they even occur (like provisioning additional servers to meet the increased demand the app will experience within the next hour) is much better than clearing out the rumble in the storm’s wake, don’t you think?
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 LogReduce feature to siphon through the lake of contingent data to find inconsistencies and anomalies, highlighting the possible issues. Combining this with the time graphs helps visualize the patterns and better define both the “normal” system behavior and the peak loads or other points of interest.
For example, should the daily code deployment (the app code itself, the data sets, the config files and the testing runs of all the above) take 4 seconds for the last 12 months, it is likely to remain the same in the future (if there are no major infrastructure updates, of course). However, if we note the process takes more and more time, we can look for the reason for the change. It can be either the growing data set volume or the no longer efficient testing or the no longer relevant config files, etc. Spotting (and resolving) such problem early on would definitely save huge resources in the long run.
Aside from such mostly self-explanatory usage of the predictive analytics in DevOps production environment monitoring, there are quite a lot of other use cases:
Application delivery tracking, using the DevOps tools like Git, Jira, Ansible, Puppet, etc. to trail the flow of the delivery process and uncover the anomalies and patterns in it. DevOps engineers can detect unexpectedly huge volumes of code or prolonged build times, low release speed and any other bottlenecks or waste in the software delivery workflows.
- Application quality enforcement. Once the testing tools deliver the output of the next testing run, the ML algorithms can detect the brand new errors, alert the testers of the case — and, sometimes, even compose a test pattern library to speed up the process of fixing those bugs. Such an approach greatly increases the efficiency of testing, resulting in higher application quality and shorter time to market.
- Application delivery security. Usage patterns are effectively our digital fingerprints. Analyzing the normal activity of legitimate DevOps engineers helps create the models of appropriate behavior. The ML models can they detect the anomalies and predict the potentially malicious usage, thus helping to stop the possible security breaches on the go. This obviously results in mitigation of millions of potential damage.
- Application performance in production. The same goes for the app’s normal performance patterns. With time, the Machine Learning tools can create a kind of a “portrait” of the app’s normal performance. After that, detecting any fluctuations lead to automatic provisioning of additional resources during the peak loads — or removing the excessive ones during the idle periods. This also applies to detecting the beginning of DDoS attacks or issues like memory leaks.
- Reduction of alert storm floods. Monitoring a plethora of systems and apps in production usually results in real alert storms. While some of these alert messages are crucial, filtering them out of the stream is quite a laborious task. However, such logging helps establish the patterns that lead to issues and highlight the very first alerts for each malfunction. After that, the normal alerts can be neglected, so only the crucial messages are escalated to the DevOps teams. This is one of the most useful applications of predictive analytics in DevOps.
- Production failures prevention. Another important benefit stems from the previous point, and it is the ability to prevent major production failures by reacting to early triggers. This helps build streamline workflows that enable resilient IT infrastructures operated with topmost efficiency. Avoiding problems instead of fighting the consequences can help save a ton of money, effort and time.
Final thoughts on using the predictive analytics in DevOps workflows
As you can see, imbuing the DevOps workflows with predictive analytics provides immense benefits for many aspects of the software delivery lifecycle. From reducing the waste in the software development and all the way up to stopping the DDoS attacks and enforcing minimal TTR (time to recover) from major failures — implementing the predictive analytics is an important step for any company that aims to utilize the DevOps services efficiently.
Does your company have any firsthand experience and answers on how to use the predictive analytics in DevOps environments? Did we miss any interesting use case or would you like to implement such approach for your business? Tell us in the comments below!
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