How To Address Complex DevOps Challenges With AI and ML?
Digital strategies of automating tasks in enterprises are catalyzed by technology. In this increasingly service-oriented world, the collaboration between development and operations staff is essential at every stage in the lifecycle of software development and deployment.
When application developers build and deliver software and cloud services, AI and ML teams try developing models that can transform your business. Being an application-centric paradigm for application development and cloud services, DevOps focuses on the infrastructure to support the application, the tools required for automation, the release process for QA and production, and so on.
AI and ML In Transforming DevOps
AI and ML solutions can transform how DevOps teams develop, deploy, and organize applications. The major challenge associated with DevOps initially was the tracking, organizing, and managing large volumes of data and computations in day-to-day operations and required umpteen number of resources.
As DevOps has become AI-driven, the complexity is reduced considerably in addition to improved data accessibility, increased implementation efficacy, as well as improved utilization of resources.
How AI and ML Can Transform DevOps For The Better?
DevOps methods generate large data sets across the lifecycle of application development, starting from development, moving on to deployment, further to application performance management. The advent of Machine Learning and Artificial Intelligence has pushed enterprises to harness these data sets to enhance the DevOps objective of end-to-end automation.
In a nutshell, the following are different ways in which ML can optimize the current DevOps scenario:
These are some ways in which AI and ML can and will transform DevOps:
- DevOps, combined with the data requirements of AI, can increase the speed of delivery of applications.
- AI brings three different characteristic features. They are self-learning, prediction, and automation. These features can enhance current DevOps practices such as Continuous Integration (CI) and Continuous Deployment (CD) as well.
- AI and ML fuel the available data sets with self-learning capabilities, making AI and ML techniques are significantly useful when integrated into the DevOps processes.
- When application code is developed, AI and ML keeps track of the scope of customer experience and analyzes customer feedback, by testing in different scenarios.
- AI and ML can enhance production performance by tracking history.
- With AI and ML integrated into the DevOps processes, the DevOps teams can derive crucial insights into the performance of the code.
- AI helps to manage the large chunks of data being generated in DevOps environments.
How AI And ML Address Complex DevOps Challenges?
AI/ML assists the DevOps teams to focus on creativity and innovation by eliminating inefficiencies across the operational life cycle. It enables teams to manage the amount, speed and variability of data. This, in turn, results in automated enhancement and an increase in the DevOps team’s efficiency.
Let us walk through the major ways in which ML and AI can address the complex DevOps challenges at hand:
- Log Analysis
Log Analysis involves large chunks of data being generated at a rapid pace. It can hence be the best first-rate debugging tool for DevOps. Log Analysis uses various ML techniques. For instance, it makes use of supervised techniques to classify data such as taking in large amounts of unstructured data and clustering them into meaningful data sets. Any data points falling outside the regular clusters are identified and reported. Additionally, it can predict the possible outcome of an activity. For instance, at the occurrence of a cluster of servers, log analytic tools analyze the probability of related services failing and hence gives you ample time to find a proper backup.
Log Analytics tools train ML algorithms to analyze logs. In addition to processing large volumes of data quickly and efficiently, Log Analysis can be used to enhance security, performance, speed, etc.
- Predicting Failures
Enterprises have numerous servers and more the number of servers, higher are the failure rates. Similarly, any crucial failure to a specific tool or area in DevOps can affect the delay cycles and thereby the entire process. With the data sets available, ML models can predict failure chances. For instance, if an occurred error has produced certain readings, AI can be used to read these patterns and predict future chances of server failures and thus reducing or preventing downtimes.
- Managing Alerts
DevOps systems are often flooded with alerts that require to be read and acted upon immediately. AI analyzes these alerts in real-time and categorizes them with priorities. This helps in the smooth functioning of the system as a whole.
An AI model can automate many repeatable tasks in DevOps and speed up the entire process effectively. AI-powered automation reduces human intervention to the least and helps developers focus on core tasks. This prevents errors promptly.