Server logs take up gigabytes of storage per week when there is too much data to manage. Much of data that is generated in DevOps processes is on application deployment. Error messages, server logs, transaction traces are resulted by monitoring an application. The best way to analyze this huge scale of data in real-time is to use machine learning. Let’s visit into how machine learning is used to bolster DevOps practices:
1. Looking beyond thresholds
DevOps teams rarely ever analyze the entire data set as there is a plethora of data. For this reason they set thresholds as a condition for action. They are essentially concentrating on outliers rather than focusing on huge chunks of data. This is a problem because outliers generally don’t paint the detailed picture and only gives indications. Using machine learning applications can be trained on all sorts of data. They can arrive at conclusion after looking up everything.
2. Learn from the history of data
One of shortcomings of DevOps team is that they don’t improve upon their mistakes. Apart from a short description of the problem encountered and what the action was to counter it DevOps professionals don’t really use that much information. Machine learning systems can help in analyzing the data to show what really happened in the recent time. It can look over from daily trends to monthly trends and give a bird’s eye view of the application at any point of time.
3. Reading between different monitoring tools
DevOps professionals most often use more than one monitoring tools to view and act upon data. Each tool has its own way of monitoring the application as per its parameters on various grounds like the performance and health of the application. The problem lies in garnering insights from all of these tools as a whole. Machine learning systems can take as inputs from all these tools and paint a holistic view of the application health in a much better way.
4. Measuring orchestration
If you want to adequately measure your orchestration process then machine learning can be used to determine as to how efficiently the team is performing. Shortcomings may result as a consequence of poor orchestration. Hence looking at these characteristics can help with both processes and tools.
5. Foresee a fault
This identifies with investigating patterns. In the event that you realize that the monitoring systems deliver certain readings in the event of a failure, a machine learning application can search for those patterns as a prelude to a particular sort of fault. In the event that you comprehend the underlying cause of that fault, you can find a way to evade it from happening.
6. Drill down to the root cause
Giving groups a chance to set right a performance or availability issue bodes well for application quality. Most often, groups don’t completely research failures and different issues since they are centered on getting back online as soon as possible. On the off chance that a reboot gets them running fine, the root cause essentially gets lost.
The confluence of technologies happens more often than we realize. Without the advent of big data, AI and machine learning models would just have remained as models and would never be implemented. Cloud and IoT are obviously related to each other. Similarly the real time effectiveness of machine learning systems are used by the DevOps processes which provide agile software development. There is huge ongoing research in this field and it should not come as a surprise if this fusion creates a new technology which will leave lasting mark in the field of technology.