The DevOps environment currently faces great challenges: large volumes of code, excessive compilation times, very slow software publishing ratios, late check-ins being just a few of these.

The application of Machine Learning techniques on activity data from different DevOps tools (Jira, Git, Jenkins, SonarQube, Puppet, Ansible, etc.)

together with data obtained in production, can significantly reduce the effort and cost of the product life cycle, improving the allocation of resources and avoiding the continuous changing of tasks.

Our client, through our Enabling services, has identified the potential of applying Machine Learning techniques on massive data sets for pattern detection, identification of inefficiencies, risks and potential failures in key aspects of DevOps. 

Through this innovation, we intend to go a step further in providing feedback and establishing the basis for a semi-automatic (potentially fully automatic) adaptation of DevOps environments to solve inefficiencies, risks and problems.