Imagine getting a complaint call from an irate customer or a higher authority regarding an app crash or a failed credit card transaction. Sounds familiar? This is a case of nightmare in every DevOps professional’s life. You will immediately have to access the log management system to find hundreds and thousands of messages logged at a particular time frame. This huge data set is practically impossible for a human eye to review line by line. Now, what do you do?
This is where a centralized real-time log analytics system comes into picture. Artificial Intelligence (AI) helps in troubleshooting this scenario using precision and pace to find the required data through Big Data streaming. AI-driven DevOps transforms the way teams manage applications, making it easier to monitor a highly complex DevOps environment.
Most IT organizations these days boast of a 24×7 presence online to cater to the woes of their customers. The IT operations and DevOps team work round the clock, shouldering the core responsibility of facing challenges and solving problems to find a single piece of data logged across large streams of Big Data.
AI is not just a buzzword any more; it has steadily become a necessity across industries and organizations worldwide. In an economy that depends heavily on traditional infrastructures, cloud, and modern applications, there is a demand for proactive management of networks to offer superior customer experience – a demand that is a necessity, but not easy to manage.
Intelligent automation is not just basic level of automation and coordination of manual tasks, it goes a tad beyond extending to adaptive techniques towards creating a completely autonomous system. The traditional, classic, and new-age agile DevOps techniques will evolve with the help of AI to support the requirement of current digital systems.
For example, with regards to traditional DevOps solutions, automation is limited to scripting. Due to rapidly changing environments and applications, maintenance of scripts become difficult and often need to be manually updated. However, in contrast, digital businesses these days demand a kind of automation that can be dynamically adapted, which can also self-heal on need basis.
Related: Enabling more Enterprise Agility with DevOps on the Cloud
Automation for classic DevOps solutions usually drives on rules that are static and predefined. A simple example of this is the statically-defined criteria required for the promotion of an application built through various stages of the pipeline. New age systems consider this to be a limitation and require the automation system to look beyond old data and at the same time keep on learning from the available data, making intelligent predictions and suggesting suitable course of action.
Similarly, DevOps for IoT systems is going to evolve tremendously as the system generates enormous amount of data requiring vigorous data mining and adaptive techniques that traditional tools of lifecycle automation cannot provide. The levels of automation adoption might differ depending on the complexity of skills to organizational challenges.
AI will impact the DevOps environment, bringing out the following positive outcomes:
Efficient management of DevOps pipeline
AI helps overcome the challenges of building a complex DevOps pipeline and managing it efficiently. With the help of learning and machine algorithms, professionals can smoothly manage the process, making data available to the right person at the right time, irrespective of the business context.
Improved access to data and decision making
One of the biggest concerns that the DevOps team faces is the lack of unregulated data accessibility. AI addresses this concern with the release of data from its formal silos, collecting data across multiple sources and organizing it for repeatable and reliable analysis. This leads to effective and efficient troubleshooting and improves the real-time decision-making process.
Related: Kubernetes and the future of DevOps
Risk reduction
Threats that loomed large over high-profile websites and multinational players are now attacking SMBs and small-scale servers. AI-driven centralized log analytics system helps to identify and resolve any critical issues and possible potential threats, increasing the overall operational management.
Resource optimization
AI with its competence to automate highly complex routine and repeatable processes minimizes the need of human input and additional resources. Humans aka employees are free to focus on tasks of higher creativity and importance.
Improved customer service
AI acts as a catalyst in making the DevOps environment effective. Identifying the inefficiencies and bottlenecks of the system, AI helps improve the functioning of the system. This leaves negligible room for error, offering faster and efficient services, leading to superior customer satisfaction.
Though the scenario of AI-driven DevOps seems to be far-fetched and simultaneously feasible, it is always better to embrace the future and find the silver lining. It is important that more and more organizations optimize their DevOps practices powered by AI. AI is sure to revolutionize the software development and deployment processes, however, patience is the key to let it work wonders for your business growth.
Contact us if you want to know the possibilities of how AI-powered DevOps can transform your business.
