Machine Learning Development vs Traditional Programming: A Comparison

Explore the differences between machine learning development and traditional programming. Understand the impact on the tech landscape through this comparison.

The rise of machine learning development might tempt you to select this option when your business needs a new website. It offers innovative solutions such as dynamic pricing and chatbots. However, is machine learning development the best choice for your business needs?

To determine whether machine learning models can solve your problem, you need to examine the features of machine learning and its predecessor, Traditional programming.

In this blog post, we'll compare machine learning vs traditional programming and show you examples of each so that you can make the best decision for your business.

What Is Machine Learning and Programming?

Traditional programming and machine learning development are two ways that software developers use to create programs. Software developers offer these services to allow you to input data which the program will process to give you a solution.

More specifically, developers use complex algorithms to create machine-learning models. These models use data to learn how to semi-independently solve problems and improve over time as developers train and adjust it. This approach to problem-solving allows it to:

  • Give Suggestions

    Machine learning models can study a data set and, from this information, suggest what you should do next. For example, they could suggest you write "Good Morning [Name]" at the start of an email since they have studied your emails and know that's your preferred greeting.

  • Give Predictions

    By analyzing data sets, machine learning models can predict future trends. For instance, if a company's stock price has grown by an average of 0.3% for the past 4 quarters, it may predict a similar growth for the next quarter.

  • Generate Data

    You can generate data using the information given to it. Therefore, if you give it millions of pictures of animals, you can ask it for a picture of a specific animal, and it will use information from the pictures of that animal to generate a new one.

  • Recognize Patterns

    The models can analyze data sets. While doing so, they'll categorize the information and find common links. This allows companies to use them to recognize normal behavior in financial transactions. In turn, this allows them to spot irregularities which could be signs of fraud.

Meanwhile, traditional programming relies on simpler code that has very specific rules to give you an answer. Due to the nature of these codes, they're best suited for basic operations.

Traditional Programming vs Machine Learning Development: Explore the Differences

Though these are both ways to create software, they're used for different reasons since their features aren't the same. Here's a quick overview of their differences:


Machine Learning

Traditional Programming

Data Required

Unstructured/Structured and Static/Dynamic

Structured and Static




Data Dependent




Complex Operations

Simple Operations

Let's explore these differences to understand better how these programs can benefit your business.

The Type of Data Required

The kind of data you need to input into these programs is different. With traditional programming, you need structured and static data. Meanwhile, machine learning models can use that type of data as well as dynamic and unstructured data.

Therefore, the data required to solve a problem affects which program you can use and which will be more effective. For instance, while you could use traditional programming to answer customers' questions on your website, it would be solely based on the set of questions and answers you provide them. Meanwhile, machine learning could use natural language processing to create a chatbot that can understand your customers' specific questions and answer them.


The way developers create programs traditionally differs from machine learning development, affecting their code's flexibility. If you needed to change a program, a developer would have to alter the code manually if it was a traditional program. On the other hand, you can more easily retrain machine learning models to adapt to a new problem and give you a solution.

For example, you could use traditional programming to create a website. However, if you wanted to change its appearance, developers would need to recreate it manually. Meanwhile, you could retrain machine learning with data on customer preferences and trends for it to change aspects of your websites.

Data Size Dependency

Traditional programming and machine learning differ in the quality of their output depending on the data they have access to. This is mainly due to the fact that machine learning requires large sets of data to learn how to solve problems correctly. The more data it has access to, the more accurate the solution becomes. Conversely, traditional programming doesn't require a large data set. It mostly relies on the code to resolve your problem.

For instance, let's say you want to offer deals for your online customers. You could use a traditional program to calculate how much of a percentage you will offer while still making a profit on the sale. Alternatively, you could use a machine learning model to display the optimal deal to consider your customers' social media, past preferences, online trends, etc. However, your machine learning model might not give you accurate answers if you have little access to data, such as with a new company or an innovative product.


Another difference between these two programs is how you'll most likely use them. Traditional programming is rule-based, so it can only solve problems as developers designed it. In fact, developers typically only use it for repeatable tasks requiring a simple process, such as a calculation.

Yet developers create machine learning models to improve over time and to gain insights from the data sets used to train them. Therefore, you can use machine learning to solve more complex problems.

For example, you can use traditional website programming to calculate your potential clients' insurance premiums based on a set of data they provide you. However, machine learning would be more suitable for calculating its optimal price since it can also take into account more information from other sources.

Machine Learning Development vs Traditional Programming: Which Is Right for Your Business?

Ultimately, which program between machine learning and the original traditional programming will depend on your problem. Traditional programming may suit you if you can solve your problem through a simple process with no variation. But if it's a complex problem that requires taking in data from various sources to make a decision or suggestion, then you should select machine learning.

Here are examples of applications of these two types of software:

Machine Learning Development

Traditional Programming

Statistical Models

Simple Calculations

Pattern Recognition

Data Sorting

Fraud Detection

Spam Filter

Recommendation Systems

Designing Data Bases


Traffic Lights

Dynamic Pricing

Data Searching

When you compare machine learning and traditional programming, it's clear that they're quite different despite both being ways to solve problems through software development. The differences in these programs affect the way you use them. Therefore, it's crucial for you to understand their differences before you outsource a software development service.

Traditional programming will work if you can solve your problem with a simple process and static and structured data. Meanwhile, machine learning, which provides solutions such as chatbots and fraud detection programs, can solve your problems by studying a variety of dynamic and unstructured data sets. So, consider these differences when you decide which kind of program you wish to purchase.