Validation data: The key to reliable AI development

When you train an AI model, you want to ensure that it not only performs well on the training data but also remains reliable on entirely new inputs. This is where validation data comes into play. It is an essential tool for evaluating your model's performance during training and making it fit for the real world.

In this article, you will learn what validation data is, why it is so important, and how to effectively use it to maximize the quality of your AI.

What is meant by validation data?

Definition

Validation data are a separate dataset used during the training of an AI model to verify its performance. Unlike training data, they are not used to directly train the model. Instead, they help assess how well the model generalizes—how it responds to new, unknown data.

Difference from other datasets

  • Training data: This data is used by the model to learn patterns.

  • Validation data: They are used to monitor the model's progress during training.

  • Test data: At the end of training, this data is used to evaluate the model's final performance.

Why is validation data indispensable?

Validation data are a central component of any AI project, as they help avoid common issues such as overfitting or poor generalization.

1. Protection against overfitting

Without validation data, your model might

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Zero-Shot Learning: mastering new tasks without prior training

Zero-shot extraction: Gaining information – without training

Validation data: The key to reliable AI development

Unsupervised Learning: How AI independently recognizes relationships

Understanding underfitting: How to avoid weak AI models

Supervised Learning: The Basis of Modern AI Applications

Turing Test: The classic for evaluating artificial intelligence

Transformer: The Revolution of Modern AI Technology

Transfer Learning: Efficient Training of AI Models

Training data: The foundation for successful AI models

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T

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V

W

X

Y

Z

Zero-Shot Learning: mastering new tasks without prior training

Zero-shot extraction: Gaining information – without training

Validation data: The key to reliable AI development

Unsupervised Learning: How AI independently recognizes relationships

Understanding underfitting: How to avoid weak AI models

Supervised Learning: The Basis of Modern AI Applications

Turing Test: The classic for evaluating artificial intelligence

Transformer: The Revolution of Modern AI Technology

Transfer Learning: Efficient Training of AI Models

Training data: The foundation for successful AI models

All

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C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

Zero-Shot Learning: mastering new tasks without prior training

Zero-shot extraction: Gaining information – without training

Validation data: The key to reliable AI development

Unsupervised Learning: How AI independently recognizes relationships

Understanding underfitting: How to avoid weak AI models

Supervised Learning: The Basis of Modern AI Applications

Turing Test: The classic for evaluating artificial intelligence

Transformer: The Revolution of Modern AI Technology

Transfer Learning: Efficient Training of AI Models

Training data: The foundation for successful AI models

All

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

Zero-Shot Learning: mastering new tasks without prior training

Zero-shot extraction: Gaining information – without training

Validation data: The key to reliable AI development

Unsupervised Learning: How AI independently recognizes relationships

Understanding underfitting: How to avoid weak AI models

Supervised Learning: The Basis of Modern AI Applications

Turing Test: The classic for evaluating artificial intelligence

Transformer: The Revolution of Modern AI Technology

Transfer Learning: Efficient Training of AI Models

Training data: The foundation for successful AI models