Episode 44: Why Building AI Is Just the Beginning — Understanding the Importance of MLOps

AI Is Closer Than Ever—And So Are New Challenges

As AI becomes more integrated into our daily lives, building machine learning models has become much more accessible than it used to be. With the right tools and some technical knowledge, many people can now try their hand at creating these models.

However, keeping a model running smoothly in real-world settings over time is a different story. Questions arise, such as: How do we ensure it keeps working correctly every day? Who fixes it if something goes wrong? And what happens when it becomes outdated?

These kinds of challenges led to the development of a concept called “MLOps.”

What Is MLOps? The Framework That Keeps AI Running

MLOps is a combination of “Machine Learning” and “Operations.” In simple terms, it refers to the practices and systems that help ensure an AI model continues to function reliably after it’s been built.

One key point here is collaboration. The people who develop the models (data scientists) need to work closely with those who manage the infrastructure and keep things running (engineers). Only when both sides are aligned can AI truly perform well in real-world environments.

MLOps provides the foundation that supports this entire flow—from development through deployment and ongoing maintenance.

Understanding MLOps Through a Weather Forecast App

Let’s take a closer look using a concrete example. Imagine a weather forecast app that uses AI to predict tomorrow’s chance of rain. This AI learns from past weather data and produces forecasts accordingly.

But as seasons change or unusual weather patterns emerge, the accuracy of its predictions may decline. When that happens, the model needs to be retrained using newer data. This involves collecting fresh data, organizing and processing it again, then feeding it back into the model for learning. And this isn’t just a one-time task—it needs to be done regularly.

This is where MLOps comes in. It helps automate this entire process—from gathering data to updating the model—and ensures that each step can be repeated consistently. As a result, we can maintain an environment where AI continues working effectively without relying heavily on manual effort.

That said, MLOps also brings its own set of challenges. These systems can become complex, and even small changes might cause unexpected issues. Another difficulty unique to AI is figuring out exactly when—and why—its performance starts to decline.

A New Era of “Maintaining While Using”—The Responsibility Behind MLOps

Even so, MLOps will only become more important going forward. That’s because as more companies adopt AI technologies, there’s growing awareness that simply building something isn’t enough—we also have a responsibility to maintain it properly.

If we compare this to human life, MLOps plays a role similar to regular health checkups or maintenance routines. It’s not about creating something and walking away; it’s about continuing to care for it over time. That mindset captures the essence of what MLOps is all about.

Although often invisible from the outside, this behind-the-scenes framework quietly supports many aspects of our daily lives. Without it, most AI services wouldn’t last very long. That’s why some even call MLOps “the infrastructure of the AI era.”

In our next article, we’ll explore another emerging concept closely related to MLOps: LLMOps. Stay tuned.

There’s something reassuring about systems that keep working quietly in the background. And by learning more about these supporting technologies and ideas bit by bit, we may start seeing new ways of engaging with AI in our own lives.