Before we adopted the DevOps approach, deploying code to servers manually was simultaneously thrilling and highly risky. Every time we finished a deployment, the team would wait anxiously in Slack to see whether the system would “go up” successfully or crash.
Once we moved into the DevOps era, things began to stabilize. CI/CD pipelines took over manual tasks, tests ran on every change, and rollbacks became one-click operations. Developers no longer had to wake up at 2 a.m. to apply hot fixes the old way. At that time, we thought, “This is the ultimate,” but actually — it was just the beginning of enabling the system to become intelligent.
DevOps: From Manual → Automation
DevOps is about uniting dev and ops so they think alike: shifting from “finish and throw over” → “finish and maintain.”
Its primary goal is automation. Every step should live in a pipeline — from build, test, deploy, to monitoring. No manual work — because most errors come from humans.
However, even if the system can deploy itself, it still doesn’t think on its own.
MLOps: From Automation → Intelligence
Once our team began incorporating ML models, it became clear that DevOps alone was insufficient.
Data scientists develop models in notebooks. But how should they be deployed in production? Which version is more accurate? How do we detect drift in models over time?
That’s where MLOps becomes essential. MLOps handles:
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Versioning models
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Automating pipelines from training to deployment
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Monitoring accuracy and alerting when performance degrades
In effect, not just deploying code, but deploying intelligence that can learn.
AIOps: From Intelligence → Self-Healing
AIOps takes it a step further by bringing AI to system operations. Instead of manually reading logs all day, the system starts “reading its own logs” and alerts us before things break.
In some cases it can auto-restart services. In others, it suggests root causes better than humans. While not perfect yet, each time our system fails and we feed feedback back in, it becomes a little smarter.
So the system grows in intelligence in tandem with us.
Making AI Smarter Together >> Read more
Deploying Intelligence = Deploying a Self-Learning System
Today, deploying code has become the baseline. The real goal is deploying understanding — letting the system recognize failure patterns, know when to scale, and decide what alerts matter.
DevOps makes systems fast
MLOps makes them smart
AIOps makes them self-aware
In this AI-talking era, the best team is not the one that deploys fastest — but the one that teaches the system to learn best. Because eventually, we won’t just deploy code — we’ll deploy intelligence that evolves with the product.