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Data Validation

Experts from Keymakr can go through your training datasets to ensure their quality. This process helps your ML models to be faster and more effective at their job.
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What Is Data Validation?

Data validation is the process of ensuring that the data used to train ML models is accurate, consistent, and relevant. It involves various techniques to identify and correct errors in the data, as well as to prevent overfitting and underfitting.

Validation data allows new information into a model that it hasn't evaluated before making predictions, leading to more accurate results. Validation is also essential to ensure that models can make predictions on new data accurately. The quality and quantity of training data determine how well an algorithm performs while training models.

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How Does Data Validation Help
Your AI Training Process?

01.Data Validation KPIs

Data Validation KPIs

Your data can be measured on Completeness, Uniqueness, and Accuracy. Data validation is used to help specifically improve the Accuracy of your existing model. Results of manual data validation are almost always more precise than automatic validation methods.


Data that was labeled during the data validation process will be of higher quality than your original dataset - increasing your overall accuracy and improving your AI training. Specific KPIs for accuracy will depend on your dataset and are different when you’re working with images, video, traffic data, human activity, etc.

02.Achieve Mistake-Free Data through Validation

Mistake-Free Data through Validation

Data validation is crucial for industries where there is no space for mistakes and bias. Automotive, medical, aerospace, and robotics models always get new data that changes the way AI in those industries interacts with the world. That’s why it’s important to check how the model was trained and routinely update it with new data.


Relying solely on a machine learning model's prediction without validating its process may lead to catastrophic consequences. Therefore, it’s vital for developers and businesses alike to validate datasets and understand their limitations.

03.The Importance of Validation in Datasets

Validation in Datasets

To construct a robust machine learning model, it is imperative to partition your dataset into three distinct subsets: training, validation, and test sets. Neglecting this crucial step may lead to biased outcomes and an inflated perception of model accuracy.


The fundamental reason for segregating data into training, validation, and test sets lies in mitigating overfitting and obtaining an unbiased evaluation of the model's generalization capabilities. The training set is employed to fit the model, while the validation set is utilized for hyperparameter tuning and model selection. Data validation process is highlighting weak points in data and demonstrates how well or how poorly the model was trained.

Why Choose Keymakr To Validate Your Data?

Keymakr brings extensive experience in over 500 highly demanding data annotation projects across various sectors, including automotive, medicine, robotics, agriculture, veterinary, and others where bias and errors can have critical consequences.

For data validation projects, we exclusively engage our highly qualified in-house team based in Central Europe. Our proprietary data annotation platform, Keylabs, is exceptionally powerful and helps maintain a strict QA process that consists of 4 stages complete with custom sanity scripts.