Scaling Beyond the POC: Understanding the Multi-Faceted Nature of Scalability in Data and ML Systems

As far as I’ve worked on various projects, the common pattern is to start with a small-scale proof of concept (POC) that demonstrates the viability of an idea or solution. Typically, the goal is to quickly show results with minimal data, few users, and a simple setup. It’s the “let’s make it work” stage.

Then, as the project progresses, the real challenge kicks in: scaling. What works perfectly in the POC starts to show cracks when you bring in larger datasets, more users. Suddenly, it’s not just about making things work but about making them work efficiently at scale.

The term scalability gets thrown around a lot, but I’ve noticed that it often loses its concrete meaning. Many people only think of it as handling more data or users, but in reality, scalability has many facets, especially in data systems and machine learning (ML).

That’s why I wanted to take a moment to unpack it and share how I’ve seen scalability across different dimensions. In fact I could count 6 facets!

Facets of Scalability in Data and ML Systems

Although all aspects of scalability share the same idea—handling growth smoothly—they present different challenges depending on the dimension you’re dealing with.

Facet #1: User Scalability:

This one is pretty obvious. When you’re just starting, there might only be very few users accessing the system, and everything runs smoothly. But as more users come in, things start to slow down if the system wasn’t built to handle that load. You don’t want a system that works fine for one user to suddenly crumble when it’s exposed to hundreds. The key here is ensuring that the system can handle growing traffic without making users wait around for results.

Facet #2: Data Volume Scalability:

This is also quite straightforward. In the POC stage, you might be working with small datasets that can be processed quickly. But as the system scales, the data starts growing exponentially, and suddenly what used to take seconds is now taking hours—or it might crash entirely. Anyone can imagine how a simple query can work great on a few thousand rows, but when it’s run on billions of rows, everything just halts. This is where distributed systems and cloud solutions come into play.

Facet #3: Data Type Scalability:

This facet is more nuanced. At first, you might only be dealing with structured data—nice, neat tables that fit into a database. But as the system evolves, you’ll likely need to work with unstructured data like text, images, or logs. It’s not just a question of “more” data but handling different types of data without breaking the system. This often gets overlooked in the early stages, but it can become a major obstacle later on if you haven’t planned for it.

Facet #4: Model Scalability:

This one often gets ignored until you’re deep into the project. In the beginning, you might have one machine learning model running, and that’s manageable. But as your needs expand, you’ll require multiple models—different ones for different user groups or business use cases. Suddenly, it’s not just about training one model but managing, deploying, and updating hundreds of models. If you’re not prepared for this, managing these models becomes a nightmare.

Facet #5: Infrastructure Scalability:

This is another one that tends to be obvious. Initially, you might be running everything on a single server, and that works fine for the POC. But as the system grows, you need more computational power and storage. At some point, you’ll need to scale the infrastructure—moving to cloud-based solutions that can handle the load. Otherwise, you’ll hit performance bottlenecks that are difficult to overcome.

Facet #6: Feature Scalability:

This might also be a bit less obvious but just as critical. In the early stages, your system might only have a few simple features. But as it matures, you’ll want to add more functionality—whether it’s more advanced analytics, recommendation systems, or something entirely new. The problem is, if you didn’t design the system to scale features from the beginning, adding these new capabilities later can lead to expensive and time-consuming overhauls.

Key Takeaways and Suggestions for Tackling Scalability:

So far, I’ve learned that scalability isn’t just about handling more users or bigger datasets—it’s about anticipating growth across different dimensions. If you overlook any one of these, you’ll likely run into issues down the line. A few personal tips that have helped me along the way:

• Think About User Growth Early: I’ve found that the easiest mistake is underestimating how quickly the number of users can grow. It’s tempting to build something that works for a small team, but soon enough, more users pile on, and suddenly, the system can’t keep up. Using load balancing and caching strategies early on can save you a lot of headaches later!
• Go Distributed Sooner Rather Than Later: Trying squeezing everything into one system will more likely to lead to a point that it just can't handle the load anymore. Try embracing distributed systems from the start. It makes scaling data volume so much easier.
• Be Ready for Different Data Types: It’s not always obvious early on, but eventually, projects need to handle all kinds of data—structured, unstructured, logs, you name it. Designing for flexibility with data lakes can save you from having to redesign things later on.
• Get Serious About Model Management: When you’re only working with one or two models, it’s easy to manage them manually. But when I’ve had to deal with hundreds of models—each for a different scenario—things can get chaotic fast. Tools like MLflow have been lifesavers, helping track, deploy, and manage models at scale without losing mind!