In recent times, there’s been a plethora of discussions in the mainstream on how machine learning (ML) will make things better.
Machine learning has gained traction in the business in the last several years. More businesses and companies are recognizing the value of machine learning in achieving their objectives.
Businesses have previously formed DevOps teams in order to optimize their development operations and increase business flexibility. However, there are many other DevOps issues that are primarily data-related.
Accessing a vast amount of data, taking measures on it, handling alerts, and so forth. Furthermore, human activity can result in inefficiencies.
ML is primarily data-driven and can aid DevOps in a variety of ways. The evolution of DevOps will be driven by machine learning. However, humans are ill-equipped to deal with the huge amounts of data and computing required in day-to-day operations.
But, how Machine Learning can optimize DevOps. Well, that’s what we are going to discuss in this post.
But before discussing the impact of Machine Learning on DevOps, let’s quickly discuss DevOps and some challenges that needs to be addressed. So, without further ado, let’s get started!
DevOps and Machine Learning!
DevOps is an organizational strategy that facilitates faster software development and simpler maintenance of the existing deployments by combining development (Dev) and operation (Ops) teams. DevOps use is growing substantially.
The DevOps software market is expected to reach $6.6 billion in 2022, up over $2.9 billion in 2017. Machine Learning has the potential to significantly improve DevOps effectiveness.
It can improve performance by allowing for rapid developments and operational cycles, as well as providing a compelling customer experience when using these features. Data collection from various elements of the DevOps system can be made easier with machine learning technologies.
Moving on, let’s discuss the business benefits of DevOps!
Benefits of implementing DevOps
Well, there are numerous benefits of implementing DevOps, few of them are:
- Enhances development speed
- Ensures greater stability
- Able to enhance entire business’s operations
- Make easy to focus on what matters most
Considering the potential advantages of using DevOps, here are some challenges. So take a look!
- Human mistakes
- Data Inconsistency
- Not easy to choose the right approach
As of now, we’ve discussed DevOps along with their benefits and challenges!
Now, let’s move and discuss the real deal!
How can Machine Learning optimize DevOps? Top ways!
#1. Easy to predict system failures
In DevOps, a severe breakdown in one space can undermine the process and slow things down the processes.
Data-driven machine learning models aid in the prediction of errors. ML can recognize patterns and predict failure symptoms, which is especially useful when a malfunction is known to yield certain results. ML is capable of detecting signals that individuals are unable to detect. These early warnings and notifications assist the team in identifying and resolving issues before they affect the software development process.
In DevOps, complete automation is an issue because many jobs are routine and must be completed by people. These repetitive tasks can be automated using an AI/ML model, considerably speeding up the process.
A well-trained model expands the range of operations that machines can perform. ML can allow developers to concentrate more on complicated interactive challenges with less human participation. Complete automation also enables quick replication and correction of faults.
#3. Easy to correlate data
DevOps teams must simplify duties in order to function efficiently. As settings become more complex, this becomes increasingly difficult. Begin with monitoring software: Teams frequently employ a number of tools to track an application’s performance and productivity in various ways.
These datasets can be absorbed by machine learning techniques, which can then be used to detect correlations, providing the team a much more broad view of the app’s longevity.
#4. Reduces complexity
By offering a single perspective, machine learning can help to simplify tasks. All of the warnings and pertinent data generated by the devices can be viewed in one location by a developer.
This improves the existing situation, in which engineers must manually examine and correlate data using many tools. Prioritizing alerts, determining root causes, and assessing odd behavior are time-consuming procedures that rely on data. When it comes to seeking up data, having a well-organized single view can be useful.
#5. Enhances software testing process
This could occur in the realm of software testing, for example. Test results from unit testing, regression testing, functional testing, and user acceptance tests all add up to a lot of data.
Using machine learning methods to analyze the test data could reveal trends of bad coding habits that result in the tests catching too many mistakes. This data might then be used to inform software developers, allowing them to become more productive in the forward.
#6. Better resource management
Machine learning and artificial intelligence offer the much-needed ability to automate regular, repeated processes. As AI and machine learning advance, the scope and variety of information that can be outsourced expand, allowing humans to devote more time to technological innovation.
#7. Better communication
When moving to a DevOps approach, one of the largest obstacles is always communication and feedback.
Human involvement is essential, but with so much data coming through the system, teams must establish a larger range of channels to define and update processes on the go. These communication channels could become more efficient and preemptive by utilizing technological innovation, chatbots, and other ML-driven solutions.
#8. Gather data from past system’s processes
As during the design and development process, machine learning can be a huge help to developers.
It can be used to analyze prior application success in terms of compile/build efficiency, operational effectiveness, and successful testing completion. ML can also make proactive recommendations based on the code that the developer is composing.
Bonus – Makes easy root cause analysis
Performing a root cause analysis is critical for permanently resolving an issue. If the root cause is not addressed, the problem will continue to exist and affect other areas in the future. Engineers are often too concerned with getting the version out to start investigating failures.
If a superficial issue is resolved, the core cause is not identified. To uncover the root cause of failure, machine learning can take all data into consideration and look for patterns among activity and reason.
As we have seen, the top benefits of Machine Learning hold for DevOps; one thing is clear that we can see artificial intelligence and machine learning applied to different stages of the software development life cycle to improve a DevOps technique or methodology.
Apart from the above, there are numerous benefits and ways that machine learning holds. If you want to, you can also benefit from machine learning techniques to optimize your DevOps process.
Feature image credit: Analyticsvidhya