Testing (in Data & AI): Checking how well a trained model performs on data it hasn’t seen before, to see whether it will work in the real world.
Example
You keep 20% of the dataset aside and test the model on it after training.
Why Testing (in Data & AI) matters for IT interns
A model that only does well on its training data isn’t useful. Testing shows whether it really works.
Where you will hear it
You will hear this term in data, analytics, and AI work, and increasingly in general software teams too. If someone uses it and you are not sure what they mean in your project, it is completely fine to ask; every team uses terms a little differently.
Related terms
- Model: In machine learning, the result of training on data — something that takes new input and produces a…
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- Prediction: The output a model gives for new input — a value, a category or a probability.
- Prompt: The instruction or input you give a generative AI tool to get a response.
- SQL: Structured Query Language: the standard language for reading and changing data in relational databases.
- Training: Teaching a machine learning model with example data so it learns the patterns in it.
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Related guides
Frequently asked questions
What is Testing (in Data & AI)?
Checking how well a trained model performs on data it hasn’t seen before, to see whether it will work in the real world.
What is an example of Testing (in Data & AI)?
You keep 20% of the dataset aside and test the model on it after training.
Why does Testing (in Data & AI) matter for IT interns?
A model that only does well on its training data isn’t useful. Testing shows whether it really works.
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About the author
Dhaivat has 16+ years of experience in the IT industry, working on AI and cloud technologies, project delivery, and engineering leadership. He has interviewed and mentored many interns, wrote The Lost Intern, and runs the non-commercial Career Impact Initiative, mentoring students for free.