With DevOps on cloud, the line between the development and operations team is blurred, and there is an increasingly more focused push towards “NoOPs.” Or, in other words, we are looking at a future with completely automated deployment, monitoring and management of applications and the infrastructure, which means enterprises will no longer need to have a dedicated team to manage software in-house. Not surprising, this push towards automated deployment has started a debate on the future of IT Operations.
For those who provide leading enterprises with cutting-edge digital consulting and technology solutions, the reality of NoOps is game changing. Why? Because NoOps means a future where operations can be eliminated, and enterprises can realize great reduction in costs. How? With the right tools and right architecture, we can eliminate mundane, repeatable tasks, like monitoring and administration, with technologies such as Intelligent Automation and Immutable Infrastructure to auto adjust systems, adapt to dynamic market demands and achieve business level objectives.
Bottom line…NoOps is a reality and not just a fad. However, the key to getting there is AI augmented DCs. And what that means for our customers is that they will benefit by realizing more business value through improved productivity, predictable operations and costs, and flexible systems that can adapt to changing business needs.
For example, let’s delve into NoOps use cases for AI technologies of machine learning and deep learning. We are seeing these technologies increasingly applied to infrastructure and application metadata processing for predictive IT infrastructure monitoring, Ops diagnostics and issue detection, and the adoption of intelligent automation technologies – particularly runbook automation, robotic process automation and IT process automation – is allowing infrastructure changes to be performed rapidly and consistently, such as immutable infrastructure and composable infrastructure support unifying AppInfra dependencies and dynamic reconfiguration of systems and can be scripted to meet dynamic workloads.
More often than not, the traditional DCs lack integrated analytical insights between systems. Automation is used primarily to achieve operational goals, such as maintaining workload SLAs and measuring infrastructure utilization metrics. Furthermore, a high level of human involvement is required for multilevel administration and change implementations. But by implementing AI augmented DCs, insights from machine learning processing of infrastructure and application metadata with onthego adjustments of configurable infrastructure into systems can be brought together to achieve strategic goals aligned to the core business. These goals are defined at the business levels, such as drive sales revenues, reduce operating costs or improve the customer experience.
In order to get there, enterprises need to start including AI augmented DCs into their digital transformation strategy, and IT leaders and business teams must work together to identify relevant business metrics.
Personally, the future I forecast is one where with NoOps, enterprise organizations will be able to dedicate resources to perform more specialized tasks in the areas of “infrastructure as a code”, AI & ML.
As the global delivery head of Digital Infrastructure services practice, Siva is at the forefront of leading enterprise technology strategy for digital transformation objectives of customers. He has successfully built and managed a global high performance delivery team at Brillio, that powers the digital forays of leading global brands.