Machine learning in DevOps along with Predictive Analytics, IT Operations Analytics (ITOA), Algorithmic IT Operations (AIOps), and Artificial Intelligence (AI) can create a powerful synergy. But what does that mean? Successful DevOps practices can generate lots of data which can be then used to streamline workflows, better monitoring and production and for finding out faults among other things. But the problem is that there might sometimes be too much data. The data might cause server logs that might take a week to resolve. The data can be easily created. While using a monitoring tool itself, the team using the tool can create several gigabytes of data in very little time.
Too much data results in a lack of foresight. When there is too much data, the teams do not look directly at the conclusion drawn by the data but rather look at thresholds which might showcase problematic activities.Even teams with an abundance of experience are looking for problems and exceptions than deeply analysing the data in front of them. However, it is important to know what you are looking for before you start looking for it, so this behaviour makes sense. But it becomes difficult to analyse large amounts of data generated by DevOps.
A lot of the data created by DevOps processes are relevant to application deployment. While creating records monitoring an application, there are several server logs, error messages, and transaction traces created as many times as needed. Machine learning in DevOps can help to analyse the data in a meaningful and reasonable way.
As stated before, DevOps teams rarely look closely at data and instead focus on thresholds that satisfy certain conditions for actions. When they do this, DevOps teams are unable to use the large data that they collect. They entirely focus on outliers which can alert of certain problems but not inform. Machine learning DevOpscan be trained so that the data can be observed by machine learning. Machine learning applications can look at the data to see if anything is concluding which will help with predictive analysis.
Once this set up is arranged, we can then look for trends rather than faults. Once all the data is trained, the machine learning system can output more than problems. Instead, DevOps workers can now identify the trends over time which might become significant.
A lot of the data is time sensitive, and it is also time-series in nature. So, it becomes easy to look at a single variable over time. If there are many transactions going on at the same time, the response time may slow as we are doing the same thing at the same time. It becomes difficult to spot these trends with careful observation by the human eye. Even traditional analytics are not helpful. Instead, machine learning DevOps applications that are well-trained can tease out correlations and trends.
Data can be generated from the continuous integration system about delivery velocity, bug finds, metrics and other related events. Do several bugs found have a connection to the number of integrations? The possibilities for looking at any combination of data are tremendous so we can look at development metrics in a new way with Machine Learning in DevOps.
It is difficult to learn from mistakes with DevOps. Even with ongoing feedback strategy, we might not have more than a wiki that describes problems we’ve encountered and what we did to investigate them. We might have rebooted our servers or restarted the application to solve the problems and errors. Machine learning DevOps applications can analyse the data to produce the reason for what went wrong over a day, month, or year. No matter if it is a seasonal trend or a daily trend, it can analyze and pick up information at any given moment.
Many teams fail to fully investigate an issue and get to the root cause as they do not have time and need to get back online. If simple solutions like reboots can get them back online, they avoid the root cause. With machine learning in DevOps, workers can get to the root cause of the issue, letting themselves fix issues once and for all whether they are about performance or availability.
With rising ranks in DevOps, workers can look at and use more tools to view data and act upon it. Each tool will then be used to check the health and performance. But how do you find the relationship between all this data?You won’t. Instead, learning systems can take the data streams as inputs, and produce a more robust picture of application health than is available today. If there are metrics relevant to orchestration process and tools, machine learning can determine how the team is performing and if they are performing efficiently. If there are inefficient processes or actions, then you can find out the cause. Looking at the inefficiencies can help both the processes and tools.
