How to Operate Less and Innovate More Using Observability and AI
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From software engineers to CEOs, everyone wants more time to think strategically instead of tactically executing tasks. While checking those tasks off your to-do list is important, and usually essential, are they the best use of your time? Humans prefer to do rather than to think, but those million-dollar ideas come from thinking. How can we fit more time into our day to make that happen? Unfortunately, we can’t. Time is finite, and we only have 24 hours in a day. But, what we can do is take some of those tasks off our plate. And I’m not talking about through a hiring spree, but rather investing in technology that can do the work for us.
This is especially true for DevOps practitioners and SRE teams who face enormous amounts of data and customer-facing issues. Today’s business leaders are pushing for their teams to spend more time innovating and less time fixing issues, yet some leaders haven’t invested in the technology to empower their teams to do so. By bringing AI-driven observability to DevOps, these issues can be addressed proactively through automation. As a result, teams can save hundreds of hours of work per year, empowering them to innovate more, operate less, and unlock their true potential. Let’s look at a few ways DevOps pros and SRE teams can leverage observability and AI to operate less and innovate more.
The Importance of Automation
In a growing digital economy, downtime isn’t an option for DevOps pros and SRE teams as they balance the constant delivery of new services with the maintenance of complex infrastructure. And more than ever, DevOps practitioners are in a fire-fighting mode as they overcome barriers put in place by complex toolchains, data, and infrastructure. But technology could be the saving grace that allows you to carve out much-needed, extra time. Observability and AI proactively detect and resolve issues within those complex systems. It streamlines incident resolution so DevOps can meet service-level agreements and objectives, manage error budgets, and accelerate digital transformation initiatives. By leveraging AI to automate observability, teams can develop more, operate less, and ultimately deliver the agility needed to innovate the customer experience. By bringing AI and observability into the dev process, you can address these issues proactively through automation.
To understand how AI can impact DevOps and SRE teams, we have to get to the basics of AI. Let’s take a look under the hood…
Understanding What Artificial Intelligence Does
As you likely explain to your grandparents every holiday, AI is more than smart computers that threaten the world with domination. AI-driven “human” behavior is everywhere. It’s helpful, digital assistants you’re familiar with, like Siri and Alexa. AI powers self-driving cars and unmanned aerial vehicles that fly themselves. It’s also in robotics, image analysis, and bots that make you want to buy stuff or ask someone you’ve never heard of to be your friend on Facebook.
But these are things you can eventually see, hear, smell, taste, and touch. The power of AI that delivers these fruits with machines finds root in a single, non-sentient word: mathematics. AI puts math to work by executing algorithms. The idea of an algorithm actually is quite simple. Algorithm is just a fancy word that means mathematical instructions a computer can follow. So, when AI-driven observability executes an algorithm, the algorithm instructs the computer system to perform operations that automate DevOps and SRE processes.
Algorithms and AI-driven Observability
A hallmark of modern algorithms is the ability to quickly examine massive quantities of data and learn from the numbers. Much of this occurs automatically without much, or even any, intervention by humans. Under the hood, AI algorithms allow observability to aggregate data, discover information, detect anomalies, enable automated workflows, and accelerate diagnostics for DevOps and SRE teams.
When applied to a domain like AI-driven observability, a set of different specialized algorithms is narrowly focused on specific tasks. From identifying correlations between alerts and other sources to assembling the right people to resolve issues, algorithms can be customized to your team’s needs.
Moving Beyond Rules with AI
AI algorithms are able to automatically process massive amounts of data from your IT environment. And no matter what your CRO says, only AI can do this. Legacy systems that rely on rules for managing IT can’t handle the operational issues of modern systems that pump out millions and billions of data daily.
Your operational goal is to automate observability — seeing and understanding everything necessary to ensure the top performance of apps and services. By using an approach with AI algorithms, DevOps and SRE teams finally achieve automated observability and control of service continuity and performance. This gives teams the ability to spend more time developing the digital services that transform and accelerate the business — and less time operating them.
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