ChatGPT is literally an awakening for society. Comparisons are hard to make, but just like the Enlightenment, Industrial Revolution and Internet, ChatGPT will forever change life as we know it. Intelligent machines that interact like humans with high levels of confidence will impact us in almost every way. And the impact to the practice and profession of DevOps will be no exception. In fact, DevOps is ripe for the disruption that generative AI and large language models are ushering in.
Why is DevOps primed for the ChatGPT awakening?
In recent years, automation has become an integral part of every industry, with DevOps, perhaps more than other disciplines, completely embracing this. The increasing complexity of IT infrastructure, along with the rapid adoption of cloud computing and machine learning technologies, has created a need for more efficient, effective and automated DevOps practices.
In response to this demand, Kubiya.ai has emerged as the go-to solution for DevOps and AI workflow automation. In this article, we will explore how Kubiya.ai is the ChatGPT for DevOps, an intelligent workflow automation solution for the modern day IT stack.
What makes Kubiya.ai uniquely suited to take the mantle of ChatGPT for DevOps?
Kubiya.ai is an AI DevOps automation platform that helps organizations streamline their DevOps processes, capture knowledge of operations and workflows, and extend those to end-users via conversational AI. The platform is designed to simplify complex processes by automating repetitive tasks and providing real-time insights into the performance of your IT infrastructure. Kubiya.ai combines the power of embedded large language models, machine learning, and natural language processing to create a platform that is easy to use and can be customized to meet the unique needs of any organization. If you were to prompt a ChatGPT command to create a doppelganger in its likeness but with domain specific expertise in DeVOps – Kubiya would be the result.
Dev and Ops challenges – two sides of the same coin
For DevOps, Platform Engineers and other professionals operating in the space of cloud operations, there are two pains that are almost ubiquitous across all organizations. The first is that end-users (oftentimes developers and engineers) are highly dependent on operations teams and often over-burden them with repetitive requests. The second pain is that creating workflow automation and knowledge management to support these end-users with self-serve, are difficult and costly to create and maintain at scale.
How does Kubiya’s ChatGPT-like experience solve these challenges?
For end-users (e.g. devs) who often are unfamiliar with infra management and operational processes burdened as well as frustrated with constant context switching, the ability to access and invoke workflows and knowledge through familiar interfaces such as Slack, Microsoft Teams or CLI, simply by prompting their intents in natural language and having it served on demand, is a utopia of simplicity, high availability and a delightful end-user experience rolled into one. SLAs are automatically reduced to near real-time, and processes that are often dependent on a human-in-the-loop interaction can be dramatically reduced to the tune of 80-90%.
What makes this possible? LLMs that can self-train on domain specific, organizational knowledge with embeddings that can be accessed and dynamically updated via simple conversations (in Slack, etc.) along with human feedback reinforcement learning.
For the Ops and platform teams showcasing their value is harder to do and they are often overshadowed by other teams who can point to sales or new products delivers as KPIs. This is because Ops teams are tasked with maintaining existing systems and are stretched thin with endless requests coming from every part of the organization. Why is this important? Because in a world where it takes Ops several hours to create an operational workflow by code and roughly 30 minutes to do the same in a low-code editor, the ability to use a generative AI workflow builder to create the exact same workflow with a simple English prompt in literally seconds, is an absolute game-changer.
And what makes these two experiences even more powerful? A system that is able to train in the workflow metadata and associate it with organizational knowledge, triggered by you guessed it- Conversational AI. If ChatGPT is sentient, it would be shedding a virtual tear of joy right now.
So what’s next for the DevOps operator in this ever evolving world of AI? Several low hanging fruit use-cases are easy to envision from test automation, to code snippet generation and intelligent workflow automation – but that’s just the tip of the iceberg. In a future article we will discuss the various practical use-cases a ChatGPT-like experience would be able to unlock in the practice of Devops.
Until then – happy prompting everyone.