AI is the buzzword that keeps on giving, its transformative values are now being commonly applied across the industries allowing for cutting-edge breakthroughs and opportunities for technologists, scientists and educators. One area that AI is already largely affecting is that of DevOps. As IT operations become increasingly agile, IT teams look at DevOps to improve productivity and efficiency and AI has the potential to further enhance this and transform how DevOps teams develop, deliver, deploy, and manage applications.

  1. Analyze data. DevOps teams deal with mass amounts of data, and it’s almost impossible to view and analyze all of it. This means they are effectively ‘getting rid’ of important collected information and focusing on the exceptions. ML applications can be trained to determine all the data. This helps when making conclusions and predications and to ensure that DevOps teams are better informed.
  2. Identify trends. Although most DevOps teams will have an ongoing feedback strategy to help record issues encountered and what they did to solve them etc., many teams never learn from their mistakes. Applying ML to DevOps can help identify trends overtime. By determining all the data ML systems can analyze it and then demonstrate what has happened over a specific period of time. It can show daily, monthly, yearly and seasonal trends for example. With ML, we can get a bigger picture of our app.
  3. Optimization. Adaptive ML aims to take input data and optimize a specific characteristic. A good example is that of Airlines that optimize revenue by changing ticket prices various times a day. DevOps can also be optimized in a similar way by modifying the neural network so it maximizes or minimizes a single value, this allows the system to modify its parameters during the production process to gradually estimate the best result possible.
  4. Data access. A common problem faced by DevOps teams is the restriction to data access from organizational silos. AI will help free this data for big data gathering as it can collect such data from various sources and organize it neatly and efficiently for its use and analysis.
  5. Find solutions. AI can help companies know what error they are looking for in a mass of data instead of wondering why their applications aren´t working and how to fix it, which will save time and money in the long run. AI can collect data from the internet about similar issues that have occurred to other systems and identify how they solved those issues. It will then scan your system to see if it has any problems. This is done through developing an AI system that copies how a user looks for and monitors events and therefore gets an understanding of how a human interacts and uses data.
  6. Smarter business. DevOps offers organizations the opportunity to automate routine and manual tasks. AI can further enhance this automation so that employees will be able to concentrate on creativity and development.

You don’t have to look hard for AI in IT operations these days. The current landscape of technologies is dramatically changing, all the time. Developing, managing and monitoring a DevOps environment is complex and fast and involves dealing with a mass volume of Big Data on a daily basis. The pressure DevOps teams face today is overwhelming, and increasingly dynamic environments have called for the need of artificial intelligence to help manage, analyze and apply mass volumes of data, tasks our human mind and capabilities are often unable to do efficiently. AI allows for automation across processes, transforming strenuous manual tasks and generally making our lives easier. AI is the tool that will help bridge the gap between humans and machines. The goal of ML in DevOps is to optimize and improve its processes from start to finish. ML and AI can process and analyze data in real time and provide solutions and predictions that DevOps teams can apply to optimize their processes and get a bigger picture of their app.

Written by: Brintias team