Data Validation

What is Data Validation? Why is Data Validation nessecary in the software development process?

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.

Data validation KPIs

Data validation KPIs to insure software quality

Data validation is used to see the progress of improvement of the existing model. Results of manual data validation are more precise then the automatic validation. The difference in this case is very settled.

Data that was labeled during the data validation process should be labeled better then the data from the model. That’s why the accuracy is the number one in this case.

Data validation for safety and mistakes detection for
industries like automotive, robotics, medical, aerospace

Data validation is crucial for life mission-critical industries where there is no place for mistakes and bias. Automotive, medical, aerospace and robotics are renewed with new data non-stop, that’s why it’s important to check how the model was trained and work with the new data.

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

safety and mistakes detection

Training dataset

Training dataset - what is it initially

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.

Test dataset is a part of the data to test how the model is trained, not visible for company’s staff

The test set serves as an independent evaluation of the final model's performance, reflecting its ability to generalize to unseen data.

Test dataset
Validation dataset

Validation dataset as a last step before your product is ready to be presented to world

Data Validation is a final and core part of machine learning model’ training. Data validation process is highlighting weak points in data and demonstrates how well or how bad the model was trained.

Why is Keymakr the best company to validate your datasets?

Keymakr is the ideal choice for validating your datasets, thanks to our 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, who perform manual data annotation.

Our preferred data annotation platform, Keylabs, is exceptionally efficient, but we are also open to using other platforms if needed.

To kickstart your project and achieve optimal KPIs, let's begin with an informative call to clarify your needs and objectives.



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