How AI and Machine Learning are the Next Evolutionary Step for DevOps
Business + Managementdevopsposted by ODSC Community August 26, 2020 ODSC Community
The advent of Machine Learning (ML) and Artificial Intelligence (AI) has changed the way we perceive DevOps. It is providing the type of DevOps that is considered to be the need-to-have framework. For many software development companies, it is crucial to use AI and ML with DevOps to ensure the uninterrupted delivery of high-quality applications and features. Sogeti and Capgemini predicted that by the end of 2020, there will be extensive use of Artificial Intelligence across all the areas of DevOps. AI is being infused in testing and operations to bring efficiency in the detection of problems, AI can also be a great help in the enhancement of DevOps.
Artificial intelligence and machine learning are the two terms used reciprocally and perceived similarly, however they are different. In simple words, AI is an extensive concept of computers being able to complete a task to reduce human burden. On the other hand, ML is an application of AI, which allows machines to learn from large data. Effective web search, voice recognition, automated cars, games are some of the fields that have applied ML to improve user experience.
[Related article: DevOps to DevSecOps: All about the Journey!]
What is DevOps?
DevOps is a set of practices that includes Development (Dev) and Operations (Ops), it is the union of process, people, and technology to ensure continuous delivery of values to customers. For example, a company has a wide range of products that are being used by customers from all over the world. To keep up with the customer’s requirements and complaints, the development and operations team must work together. DevOps thus ensures fast and short release cycles that can get new applications and features to customers quickly and reliably. It is also about team coordination feedback loops for continuous improvement, this helps in capturing the release bugs as early as possible thus reducing the testing time.
How Machine Learning and Artificial Intelligence are applied to DevOps culture?
Optimizing functions of a DevOps environment
DevOps and Machine Learning share a powerful alliance with related capabilities like predictive analysis, algorithmic IT operations, Operations Analytics, and AI. Introduction of Machine Learning into DevOps has brought benefits such as checking highly complex data sets. Detect patterns and antipatterns, uncover new ideas, repeat and refine queries with the speed and perfection. For example, delivery processes can be tracked with various DevOps tools, these tools produce a lot of data and any kind of error in this data can be detected by the application of Machine Learning. Large code volume, slow-release rate, and long built times are some of the issues that can be put in check using Machine Learning. Machine Learning can also review the QA results and figure out errors and present a better testing pattern based on these findings. It can track the pattern in the behavior of the development and operations user and detect irregularities indicating malicious activities like stealing intellectual properties, deploying authorized code, etc.
Management of codes can become easy and efficient with the help of Machine Learning, large data volumes, transaction records, and the number of users can be analyzed by Machine Learning. It can point out the errors concerning normal values. It can also detect errors in the normal proceedings, and wisely analyze previously released logs to find out the issues with the new release. ML can read the previously used good patterns and plan out the best configuration to maximize performance.
Tracking user behavior and security
AI and ML can help us optimize our application by analyzing usage data and security threats. It can identify the modules and functionalities of an application that are being used the most so that we can focus our efforts on improving user experience in those areas. With the constant focus on user behavior, AI can help us to prioritize the user experience in our release planning. Tracking user behavior and taking user experience related decisions is the most crucial part of a product’s success. By leveraging the process, AI and ML eliminates the human efforts in the process and helps organizations to make it more efficient and accurate. With the help of AI, we can easily track the security threats and fortify our defenses to combat the attack before it puts the organization into any loss.
AI automation and consistency into the DevOps and thus in the release process, but some areas require human efforts to manage the process. AI helps us to automate the processes that can reduce the chances of human errors. This automation process also helps us to free up valuable resources to be used in innovative solutions. AI has the potential of self-heal problems, it can recommend solutions for writing more efficient and performant codes. It helps the development team to decide on what to be addressed next by prioritizing the anticipated impact of a change.
[Related article: How Can You Combine DevOps and Automation for Robust Security?]
AI and ML are uniquely positioned to help you enhance your team for solving problems quicker. New technologies and practices are being introduced into the IT industry every day, these new technologies run their course eventually giving way to even newer prospects. Thus DevOps is a relatively new addition to the industry and it is quite evident that with the application of ML and AI, its lifeline would be stretched substantially in an ever-improving market.
Piyush Jain is the founder and CEO of Simpalm, top mobile app developers in Chicago. Piyush founded Simpalm in 2009 and has grown it to be a leading mobile and web development company in the DMV area. With a Ph.D. from Johns Hopkins and a strong background in technology and entrepreneurship, he understands how to solve problems using technology. Under his leadership, Simpalm has delivered 300+ mobile apps and web solutions to clients in startups, enterprises, and the federal sector.