To understand GitOps for Machine Learning, it is important to understand these terms separately first.
Machine learning is one of the trending technologies that are contributing in automating the world at a faster pace. Basically, machine learning is a subfield of artificial intelligence that is capable of imitating intelligent human behavior to perform certain tasks. It is believed that it has a tendency of performing intelligent human tasks even better than humans at a certain point. It is indeed true because it can function in dynamic environment and automate those changes as well.
GitOps, on the other hand, is the combination of Git and DevOps. Git manages the collaboration of developers by tracking and applying the changes in the development to the desired state as version-control commits. DevOps, on the other hand, speeds up the process from development to deployment. You must have had the idea of GitOps now that we have discussed its origination and the functions performed by its originators. Well, to be precise, GitOps is a developer workflow for automating the operations. It smoothens the development process and speeds up the deployment by automating the time consuming manually performed tasks of developers.
Now that we know the meaning of GitOps and Machine Learning, it will be easier and clearer to understand them together.
GitOps and Machine Learning
We have come to three conclusions after analyzing the combination of GitOps and machine learning. They are as follows:
- We can improve the accuracy of deployment and pre analyze the future deployment failures.
- Make the deployment validation process simpler, more efficient and effective.
- We can assess and enhance existing quality for better deployment results.
Let’s have a deeper look into these analysis for better understanding and clarity.
We know that GitOps tracks changes in code commits and configuration. We can automate the process of continuous integration by using Jenkins. Continuous Integration is the process where changes in the source code go into the image repository. Therefore, GitOps when combined with machine learning can deliver better and faster deployment results.
Coming to the configuration changes, we need to know that they are driven by Git commits. We synchronize the files in Kubernetes after putting them in Git. There are various tools which can be used to continuously monitor and compare the applications running in the cluster with that in the Git repository. This is how Kubernetes roll back the differences between Git and the cluster. The combination of GitOps and machine learning allows developers to increase deployment velocity by ensuring multiple deployments in a day with faster and streamlined development.
Customarily, it is seen that GitOps rebuilds the packages after analyzing the differences between build package and change in the code. This need for rebuilding the package can be eliminated with the addition of machine learning in the process. It will speed up the build process by training the system to select the builds that are predicted to be a failure for a particular code change. Machine learning uses previous code changes to come up with a build selection model. The previous code changes will help the system know the pass or fail of the package build and will automate the building selection model accurately.
Machine learning is also used for security scanning. With this, we can predict the failure faster and have more time to recover the failure. For the development, we can use machine learning to filter out unwanted pull requests and save time to efficiently deploy the software.
A website is the creation of the developers. It contains many external components used by the developer to make the website fulfilling. At the time of website deployment, these external components may be defected while the website remains validated. To avoid such defects, we can use machine learning to capture the entire website as an image and further divide that image into various UX components. The learning model can be fed by the training and testing provided by the divide UX components. Once the model is set, any new UX components can be easily tested across different browser resolutions and can be fed into the model. The incorrect images will be classified as defects so we can use images to train a data model to discover the defects beforehand.
A set of training images are taken as training data by the supervised algorithm. To know about the desired function, the training data provides the learning algorithm which is used to compare with the test data.
Machine learning can also be used to select and prioritize tests in the development process before deployment. Traditionally, on a particular code change, information is extracted from the metadata by regression testing to determine which tests should be run. All the tests that need to be run can be determined by analyzing build dependencies in the code. Any change in the low-level libraries cannot be rerun due to the inefficiency of all the tests on every dependency. In testing, machine learning can help us improve the efficiency. With this, we can get a new way to select and prioritize tests before deployment. Machine learning will help the system calculate the probability of finding a regression with a particular code change in the given test. This will allow the system to make an informed decision eliminate the tests that are not likely to display the issue or change or defect or difference in the cluster and Git.
Another way in which machine learning can be used to select and prioritize the tests is that we can apply high techniques of machine learning on a great amount of data of the past code changes in the test results. The past data will help the system to learn the model from the classified features of the previous set of data. So, when the new code changes are applied, they can be run into these learned models and can be prioritized on the basis of the accuracy of results. The learned model will help analyze the further probability of a failure in the test and run those tests first to resolve the issue faster for speedy deployment. Certain tests will show both pass and fail in the results. It means there may be uncertainty of the result. In such a situation, it becomes important to segregate unstable test results from machine learning model. Machine learning can predict the probability of failure by the training provided for predictive test cases to the model. This way we can prioritize and select the tests to be run first to make faster and efficient deployment of the software.
The Bottom Line
The entire article clears the various ways in which we can use machine learning with GitOps to make the deployment of the software simpler, efficient and effective. This combination of GitOps and machine learning tells us more about the scope of technology. The scope of technology is huge. It is a never-ending discovery that opens hundreds of doors when we try to put an end to a particular discovery. Firstly, we originated GitOps from the combination of Git and DevOps. On the other hand, we developed Machine learning as a sub part of AI i.e., artificial intelligence. Now, we have found ways in which we can use these two discoveries together to form an even greater technological trends for the world. The cycle goes on and there is no sight of the shore. Our dive in the ocean of technology has been life changing so far. To be honest, this ocean is larger than we can ever imagine it to be. We will live on with changes and make life on earth automated in the coming future. The continuous growth of technology will surely take us to that phase soon.
Let us know if you want find the shore of technological sea. Talk to us!