All articles Insights · ML/DL

Machine Learning or Deep Learning? How to Choose for Your Business Problem

November 14, 2024 · 4 min read

“Should we use deep learning?” is one of the first questions we hear when a company starts an AI project. It is a fair question, and the honest answer is: it depends on your data and on the decision you want to make. Deep learning is not a better version of machine learning. It is a different tool for a different kind of problem.

Start with the shape of your data

The quickest way to choose is to look at what you are feeding the model.

Rows and columns. Sales history, transactions, sensor readings, CRM fields. This is structured data, and classic machine learning (gradient-boosted trees, random forests, regression) is usually the right starting point.
Images, audio, video, free text. Photos of parts on a production line, call recordings, contracts, emails. This is unstructured data, and deep learning is usually the better fit, because it learns the features for you.

Most real projects contain both. A field-service platform might forecast job durations from structured history and read photos of completed work with a vision model. You pick the right tool for each part.

Four questions that settle most decisions

1. How much labelled data do you have?

Classic models can learn useful patterns from a few thousand good examples. Training a deep network from scratch needs much more. The middle path is transfer learning: start from a model pre-trained on millions of images or documents and fine-tune it on your own. That is where most practical vision and language projects begin.

2. Do you need to explain the decision?

If a credit officer, a regulator or an operations manager has to understand why the model said what it said, simpler models help. You can see which inputs drove a prediction and sanity-check them. Deep models can be explained too, but it takes more effort and the explanations are rougher.

3. Where will it run, and how fast?

A gradient-boosted model can score thousands of records a second on an ordinary server. A large vision model may need a GPU, or careful optimisation to run on a phone or an edge device. Running cost matters as much as accuracy once a model is in daily use.

4. What does a wrong answer cost?

For a product recommendation, a miss is cheap. For a defect that reaches a customer, it is expensive. The higher the cost of error, the more you invest in evaluation, monitoring and a person in the loop, whichever type of model you choose.

A simple rule of thumb

We usually build the simplest model that could work first and measure it honestly. If it meets the business target, we ship it. If the gap is real and the data is unstructured, we move to deep learning. This keeps cost and risk down, and it gives you a baseline to judge the bigger model against.

What this looks like in practice

On the drone intelligence platform we built for a US manufacturer, spotting defects in imagery is a computer vision job, the kind deep learning is built for. Scheduling missions and producing reports does not need it at all. On the robotic-arm testing project, vision models read the screen while ordinary logic drives the test flows. Using each technique only where it earns its place is what keeps these systems fast and affordable.

The short version

Choose by data first, then by explainability, running cost and the price of a mistake. Start simple, measure, and only add complexity when the numbers say you need it. If you are unsure which camp your problem falls into, that is exactly the kind of question we answer in a first 30-minute call.

Frequently asked questions

What is the difference between machine learning and deep learning?

Deep learning is a branch of machine learning that uses large neural networks. Classic machine learning works well on structured, tabular data; deep learning shines on images, audio, text and other unstructured data.

Does deep learning always give better results?

No. On tabular business data, well-tuned classic models such as gradient-boosted trees often match or beat deep learning, and they are cheaper to train, run and explain.

How much data do we need to start?

It depends on the problem. Many useful classic models start with a few thousand clean, labelled records. Deep learning usually needs far more, unless you fine-tune a pre-trained model.

Planning something similar? Talk to our engineers or see our AI development services.

Keep reading