I’m often amused when I read the ad tech companies’ websites and see how they confused traditional programming with machine learning.
There is a fundamental distinction: programming spans across multiple areas such as interface, data pipelines, infrastructure automation. While machine learning is more on the data/statistics side.
Just go to the Python scikit-learn library examples page, they are around three categories: classification, prediction, decisions.
So, what are the machine learning main use cases in Ad Tech? Ad Tech is dealing with two major entities: ad and audience. For each entity, there are machine learning applications:
- Audience: identify the top-performing audience, and detect bots traffic (classification), run simulations on how combined audiences will behave (prediction), remove low-performer audiences, and create new ones (decisions.)
- Ad: categorize ads based on creatives, such as images or videos (classification), run simulations on how each audience and creative will perform (predictions), recommend creatives combinations for each audience (decisions.)
Any ad tech product that is not classifying, predicting, or making decisions/recommendations is not using ML.