This article will discuss the connection between artificial intelligence and DevOps, with specific emphasis on the fields of machine learning and deep learning. You will then learn about five ways in which DevOps can leverage machine learning methods and AI to optimize workflows.
DevOps and Machine Learning
DevOps emphasizes short development life cycles and seamless updates to applications, and one of the key tools it proposes for achieving these aims is automation. Machine learning is a domain of AI that uses statistical techniques to enable computer systems to access data, uncover patterns, and learn from the data without explicit programming to perform such learning. So, where’s the connection?
The connection emerges when you consider the enormous quantities of data produced during DevOps processes. Using machine learning tools and methods, this data can be analyzed for insights and patterns, helping DevOps teams continuously refine their processes and achieve desired levels of automation.
Neural networks underpin the functioning of any machine learning model. A neural network is hardware and/or software that is modeled on a biological brain, containing a series of connected units, known as neurons.
4 DevOps Machine Learning Uses
Improved Code Quality
Releasing high-quality applications free from bugs that severely impact functionality is of paramount importance. However, Devops also requires testing to be conducted in an automated, swift way so that the aims of DevOps can be achieved. Manual testing in rapid development environments needs to be kept to a minimum so that testing doesn’t become a bottleneck.
One of the best ways to achieve desired levels of testing automation is to use machine learning methods to help testing tools come to a more intelligent, data-driven understanding of what constitutes high quality code and applications. The end result is comprehensive and fast testing that covers all important defects, improving the quality of code making it to production.
DevOps teams use a host of tools in their work, including the Jenkins automation server, Ansible’s software provisioning, configuration management, and application deployment application, and the version control system, Git. Activity data produced by the usage of all these tools can be brought together and fed into a machine learning model which can uncover patterns and anomalies in the data that DevOps teams have little visibility over.
The result of using machine learning to analyze activity data produced by DevOps tools is that development or operational inefficiencies can be identified more accurately. Issues like spending too long on certain tasks to the point of diminishing returns or bottleneck processes can be flagged and rectified, leading to shorter, more efficient development life cycles.
Automated Alert Prioritization
Alert fatigue has entered into the common parlance of DevOps teams, such is the magnitude to which DevOps engineers get bombarded with a range of alerts from all the monitoring tools they set up in production. Email inboxes become packed full of all these different alerts without any prioritization and the end result is that the monitoring tools, which are ostensibly used to monitor applications, end up being as worthless as not monitoring at all.
Machine learning and deep learning models can manage these alerts more intelligently by categorizing, grouping them, and prioritizing them. It is in prioritization that machine learning can deliver the greatest value to DevOps in this regard by gradually learning which alerts actually warrant attention and which ones to filter out. When people only see the alerts they need to, so-called alert fatigue is minimized. Deep learning platforms can help scale up this process and make it more efficient.
Predictive Failure Response
One of machine learning’s most widely acknowledged successes is its use in industrial settings to analyze sensor data and predict the failure of machinery and other equipment used to manufacture products. This sort of predictive failure response is also possible in the context of DevOps and deploying applications to production.
Machine learning models can be trained to predict application failures in production by monitoring huge stores of production data, such as throughput, to spot otherwise hard to find problems that will eventually result in outages. Given the increasing costs of downtime, this type of predictive application failure response is of great use in DevOps teams.
DevOps teams can leverage the power of machine learning in a variety of ways to improve their workflows and optimize processes. One potential issue flagged in relation to machine learning methods is the difficulty in understanding why a given model consistently arrives at certain decisions or classifications from a set of training inputs. Newer techniques like generating Grad CAM images are beginning to answer these more difficult machine learning questions using the power of data visualization.