DevOps and machine learning are advanced technologies that tend to change the world’s future. This combination helps both professionals and individuals to grow in the market.
AI/ ML helps DevOps complete the tasks efficiently and improve the team’s performance, leading to the business’s growth. It is one of the most amazing technologies for developers because this integration can create amazing things in less time.
Every company wants to automate DevOps to create custom AI/ML layers. However, the first step is to create a robust DevOps infrastructure.
DevOps is AI-driven and helps manage the vast information and computational capacity in day-to-day operations. AI can become the primary tool for DevOps assessment, computation, and decision making. AI can transform the way DevOps teams develop, deploy, use, and organize applications to improve their performance and DevOps business operations.
Benefits of Implementing Machine Learning to DevOps
- It helps to reduce the complexity by accessing human intelligence.
- It helps automate regular and repeatable tasks to manage resources to some extent.
- It helps collect data from multiple resources and is reliable for further use.
- It manages alert storms to keep the processes smooth.
- It helps to process and identify threats.
- It can detect the effects of different patterns by analyzing users’ metrics to solve the problem immediately.
Use Cases
Tracking Application Delivery
Activity data has been collected from DevOps tools such as Jira, Git, Jenkins, SonarQube, Puppet, Ansible, etc. Applying ML can help uncover anomalies in data such as long build times, large code volumes, late code check-ins, and slow-release rates. It ensures that various wastes in software development processes are identified, such as gold-plating, inefficient resource allocation, partial work, process slowdown, and excessive task switching.
Manage Products
Machine Learning do wonders in analyzing an application in production. Implementing ML with DevOps ensure to offer greater data volumes, transactions, user count and more. With the help of ML, DevOps teams can analyze ‘normal’ patterns such as resource utilization, user volumes, transaction throughput, etc. In addition, it detects abnormal patterns to avoid DDOS conditions, race conditions, memory leaks, etc.
Securing Application Delivery
It is believed that user behaviour patterns can be unique, like fingerprints. By applying ML to DevOps, user behaviour can identify anomalies in malicious activities. It includes anomaly patterns of access to automation routines, sensitive repos, development activity, system provision, test execution and more. These anomalies are also be highlighted as users exercising “Known bad” activities, whether accidentally or intentionally. Thus ML in DevOps ensures to deliver application security to all the users to make the applications more reliable.
Triage Analytics
In this area, ML with DevOps shines more than anything. ML in DevOps can automatically detect and start intelligent triage ‘known issues’ and unknown ones. ML tools can detect anomalies in normal processing to further release logs to correlate the issues with new deployments. However, other tools can use ML to open a ticket, ML alert, and assign them to the right resources. In future, ML may be able to suggest the best fix for various issues.
Analyzing Business Impact
DevOps succeeded in understanding the impact of code release on business goals. Ml system can detect good and bad patterns by synthesizing and analyzing real user metrics. It helps to provide an early warning to the development and business teams when the application faces any problems. Early reporting can increase cart abandonment of buyer journeys.
English (United States) 
English (UK) 
English (Canada) 
English (India) 
English (Australia) 
English (New Zealand) 
English (South Africa) 
French 
German 
Spanish 
