Develop systems are responsible for handling massive sets of data as well as providing a computer and cloud computing service to massive IT based industries. It is important to understand how DevOps works and layers the data into suitable prefixes that at some point in future are made parts of apps, software and tools.
Therefore before incorporating any new technology into DevOps it is important to know that whether the technology would lead to a better understanding or a better objective for things would it be able to optimize the processes significantly or not. Same concept applies to machine learning, it is without any doubt a very impressive way to control the outcome of various processes within the DevOps environment but on the other hand the compatibility of the developed systems must be checked beforehand. It is recommended that you take part in DevOps online training to best understand various DevOps related systems.
A very common problem that still persists is the DevOps team trying to avoid a deep dive into their data cluster, they would only try to spectate the problematic nodes instead of interpreting the whole data which they have accumulated for a specific project. This is where the problem begins and the idea of machine learning can be applied because computers can't interpret things the way humans can and must oblige with the instructions they are fed with. Machine learning can be used to significantly increase the scalability and optimize the ability of develop systems to perform standard objectives.
Following are some of the ways using which machine learning can improve the way develop systems are currently working for a dedicated organization;
Stop avoiding and start looking at your data cluster
As discussed earlier DevOps professionals don't quantify all of the data they only look for the potential elements and they discard the rest of it which is a total loss. On the other hand small thresholds are brought into place which only can differentiate which means not particular data set which aligns with the threshold’s limit.
This way plenty of data is either lost or is not interpreted by the professionals at all that is why the concept of machine learning can be applied here instead of discarding all the data as it is. When it arrives the machine learning objectives can streamline differentiated process which can help interpret all of the data that is coming, dissect it, visualize it and then differentiate it on the basis of its importance and the message that it carries.
Putting forth a historical context of data
One of the biggest problem with the DevOps systems is that we barely learn from our mistakes, every breach or a cyber anomaly must be recorded in dedicated data infrastructures and then can be accessed to know whether or not we have overcome the problem or the problem still persists. DevOps most professionals don't attend to this specific need of IT industry, this is where machine learning can be of significant importance. Not only it can help to quantify the data containerize it but also ride with an open approach using which the DevOps professionals can learn from the past mistakes.
These data sets can be dissected into specified trends which would then represent the mistakes that were done either yesterday or a day before or a week ago, a month ago or an year ago. It would help to not only just have a feedback mechanism but also to act on it and recover from disasters that lies ahead learning from data trends at hand.
Get to the definitive cause of the problem
What would help the IT based professionals more; either to stick by a problem and try to solve it all to jump across it and deny its presence? Often the IT based professionals are not that concerned about determining the root cause of the problem which put them offline or off the grid, they are more engaged to find ways through which they can get systems back online and running at full throttle.
This is a grave problem of the DevOps world not being able to understand what caused the problem but trying to find ways to put things back online. with the help of machine learning different trends can be acquired which would tell briefly about the instances of a breach, which systems were affected the most, why did it happen in the first place and most importantly what are we going to do about?
Determining the overall efficiency
The concept of Devils surrounds effective collaboration in communication between various teams to be able to pull off the best of the standards and quality oriented products reaching the customers. But what if there was no mechanism of keeping tabs on the efficiency of the processes and how effectively the professionals were working to achieve the common good? Incorporating machine learning into DevOps environment with significantly help to monitor the current stats of processes and systems and to also deploy amends wherever they are necessary to make sure that the whole thing is selling in the right direction.
Learning can promise you a bright future but you'd have to manually interpret various settings so that you have a customized solution that fits the very profile of your business.
Predicting faults and learning from them
It is quite possible that in pursuit of a cyber beach the digital systems would have incorporated the reasons and other valuable data which led to the bridge in first place. This could be the master IP address of the culprit more affection chosen by the illicit cyber criminals address the bridge and what kind of data or simulated information was stolen from the secured premises. If it does store this kind of data then rest assured machine learning can extract every ounce of it and present the professionals with the very solution they didn't have access to beforehand.
DevOps process flow is extremely complicated as it is but incorporating machine learning in there would considerably help to smooth things in an effecient way.