Every day, we developers are hustling to keep up with the ever-evolving complexities of cloud infrastructure – and one of the biggest disruptions is the introduction of AI into cloud infrastructure. AWS, for example, has rolled out a wide range of fully managed machine learning services, which may soon help developers optimize their infrastructure more intelligently than ever before.
While today’s cloud automation rules are complex to configure and adjust, tomorrow’s AI tools may make resource allocation and deployment as simple as a few clicks on a centralized dashboard. In fact, quite a few aspects of cloud infrastructure are overdue for smarter automation. Here’s a quick overview of the current state of cloud automation, along with some optimistic predictions for the very near future.
An effective DevOps model requires human and machine intelligence.
The goal of DevOps is to develop and deliver infrastructure at a faster pace than traditional development – which means breaking down silos and streamlining repetitive tasks. By automating processes that were traditionally handled manually, engineers can deploy code and provision infrastructure on their own, without waiting for help from other teams – enabling them to deliver services more rapidly and reliably.
Of course, all this streamlining doesn’t come for free. Automation requires rules – and today’s cloud automation rules are like a fancy wrench, designed to fix a wide range of issues, provided the user knows how to configure them correctly. This configuration requires significant time and effort, and is always subject to human error. After the automation has been correctly configured, it takes continual adjustment to make sure it continues to work in harmony with the business’s needs and expectations.
For example, automated traffic routing between containers can reduce visibility for other developers; while automated provisioning can sometimes leave a lot of containers sitting idle, waiting for a spike in traffic. In other words, automation is only as effective as the coordination between infrastructure and development teams, and the alignment of their goals.
Modern infrastructure demands an AI-driven approach.
Today’s DevOps teams are like factory workers operating tools. Those tools can be very precise and powerful – but they’re manual tools, all the same. It’s time for developers to stop repeating mundane work, and get promoted to high-level managers who train and supervise infrastructure robots to handle the grunt work for them.
After all, it’s no secret that AI algorithms are getting smarter every year – and although it’ll be a long time before an AI can take the place of a human developer, it’s already making life easier for DevOps teams working in the cloud. API-driven services enable developers to add machine learning to any application, while smarter analytics provide clearer insights into data workflow and process orchestration.
In the very near future, AI will analyze a business’s goals, and recommend infrastructure designs and policies that serve those goals. Algorithms will intelligently configure automation tools, learning and adjusting as traffic patterns and business objectives evolve over time. AI will enable DevOps teams to plan and align their development and deployment more effectively, while minimizing time spent on manual configuration and coordination.
Here at Magalix, we often say that building and evolving infrastructure is a creative process, requiring just as much human communication as automated precision. Even so, some aspects of cloud infrastructure are in serious need of better automation tools. We believe it’s high time that AI took a more central role in DevOps.
When you’re ready to upgrade your DevOps team from manual laborers to high-level managers, we’re here to bring those infrastructure robots onto your team. Let us show you how.