There is a strong synergy between Machine Learning and DevOps and this extend to the related aspects like IT Operations Analytics, Predictive Analytics, Artificial Intelligence and a lot more. DevOps methodologies are surging high and generating large volume and variety of data during the entire lifecycle right from development to deployment to management. Hence only a reliable and robust analysis system can harness the data for achieving the ultimate goal of DevOps: automation.
Successful DevOps practices produce gargantuan volumes of data and it is not at all startling that this amount of data is very much capable of drawing insights that would prove to be helpful in streamlining workflows, monitoring productions and predicting issues. The huge volume of data gives a predictable result. Teams do not see and read the data populated directly but ensure a threshold of a particular activity to declare it problematic beyond it. DevOps teams look for exceptions arsing rather than the data individually. However, the only reasonable way to analyze this data and draw meaningful insights is via machine learning.
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So let’s find out what wonders can Machine Learning creates when gelled well with DevOps:
1. Learning From Past Mistake
DevOps teams quite occasionally commit mistakes and those issues cannot be resolved if they are in action. Machine learning systems aid them to analyze the data portray before them what has occurred in during the recent activities. It spins across trends from a particular day to a month and provides intricate details of the application at any given time.
2. See Your Development Metrics In A Different Way
It will help you to collect data on various aspects like delivery velocity, bug fixes, and continuous integration systems. You might be up for cross-checking and discovering if the number of integrations relates to the number of bugs found. Hence you can find out entire new possibilities and endless new combinations of viewing data and the development metrics.
3. Discover The Root Cause
Identifying the root cause is a major aspect and ML helps in that. It helps the teams
to fix the performance issue at one go. The teams quite often do not entirely investigate the issue and failures as they are busy getting back online. In case of a reboot, if it gets back up, the root cause is lost.
4. Measuring Orchestration
If you amongst the one who wants to monitor the process of orchestration then you can easily take the help of machine learning to evaluate the team performance. Limitations may arise because of reduced orchestration hence monitoring the characteristics aids in both the tools and processes.
5. Prediction of Fault
This goes along analyzing the ongoing trends. If you are aware that the monitoring tools produce a certain data at the time of failure generation then ML application can draw meaningful insights from those patterns as a prelude to that particular type of issue. Understanding the root cause of the specific fault will allow you can take necessary actions to stop it from happening.
6. Look Beyond Setting Threshold
There is a huge amount of data and DevOps teams hardly focus on analyzing the entire data set. Instead of analyzing they set thresholds marking it as a condition for action. Doing so they waste the majority of data they collect and focusing on outliers. However, the issue with this approach is that it will alert and not inform. Machine learning can train them on all of the data, and once in production, those applications can look at everything that’s coming in to determine a conclusion. This will help with predictive analytics. DevOps is a blooming concept and machine learning has already made its establishment quite prominent. So machine learning definitely magnifies the power of DevOps. Thereby applying machine learning to DevOps enhances its capabilities to carry out their cloud-based operations with much more finesse and efficiency.
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References: towardsdatascience, skymind, re-work, jaxenter