Machine Learning: The High Interest Credit Card of Technical Debt

Machine Learning: The High Interest Credit Card of Technical Debt

Machine learning is often touted as a way to quickly and easily improve your product or service. However, like any new technology, it comes with its own set of risks and challenges. In this blog post, we’ll explore some of the potential dangers of machine learning, and why you should be careful before taking the plunge.

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The term “machine learning” has become increasingly popular in recent years, as businesses look for ways to harness the power of data. But what is machine learning, and why is it so important?

In its simplest form, machine learning is a type of artificial intelligence that allows computers to learn from data, identify patterns, and make predictions. This can be extremely valuable for businesses, as it can help them make better decisions, improve their products and services, and automate tasks.

However, machine learning also has a downside: it can create a lot of technical debt. Technical debt is defined as the cost of future rework caused by taking shortcuts today. In the world of machine learning, this can manifest itself in several ways, such as using suboptimal algorithms, not tuned models, or not having enough data to train your models.

Technical debt can be difficult to manage, because it often requires extra time and resources to fix later on. However, it’s important to be aware of the potential pitfalls of machine learning so that you can avoid them in your own projects.

What is machine learning?

Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the construction and study of algorithms that can learn from and make predictions on data.

Machine learning is closely related to and often overlaps with statistics, optimization and data mining.

The high interest credit card of technical debt

Technical debt is often compared to a financial debt, in that it can be seen as something that needs to be paid off over time. In the same way that you would pay off a high interest credit card, you need to address areas of your code that have accumulated technical debt in order to prevent them from becoming a drag on your development process.

One way to think about technical debt is in terms of the interest payments you make on a financial debt. Just as you make regular payments on a credit card balance, you should also make regular refactoring a part of your development process. This will help to ensure that your code remains maintainable and scalable over time.

It’s important to remember that technical debt is not necessarily a bad thing. In fact, it can be helpful in getting a product to market quickly. The key is to manage it effectively so that it doesn’t become a burden on your development process.

How can machine learning help?

Machine learning can help by finding patterns in data that humans would not be able to find. It can also make predictions about future events, which can help businesses make better decisions.


In machine learning, there is a trade-off between algorithm performance and interpretability. More powerful (complex) algorithms usually perform better, but are harder to interpret. This is similar to the trade-off between having a high interest rate on a credit card and being able to pay it off quickly. The high interest rate means you’ll end up paying more in the long run, but the low monthly payments make it easier to manage in the short term.

Just like with credit cards, it’s important to be aware of the trade-offs involved with machine learning algorithms. If you’re not careful, you can end up in a situation where you have low interpretability and high error rates. This can lead to what’s known as “technical debt”, which is basically when you have to keep using a complex algorithm even though it’s not ideal because you’ve invested so much time and effort into it.

So, next time you’re choosing a machine learning algorithm, make sure you consider both its performance and interpretability. And, just like with credit cards, try to avoid taking on too much technical debt!

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