NatanMish/data_validation
Tutorial for implementing data validation in data science pipelines
This project offers a hands-on tutorial for data scientists and machine learning engineers on how to implement robust data validation at every stage of a data science project. It guides you through validating raw data from a database, data flowing into a training pipeline, and data used by a deployed model. You'll learn to ensure data quality and consistency, preventing errors that can derail models.
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Use this if you are a data scientist or machine learning engineer looking to prevent model failures and ensure reliable results by implementing data quality checks throughout your entire model lifecycle.
Not ideal if you are looking for a plug-and-play data validation tool for immediate use in production without understanding the underlying concepts.
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33
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10
Language
Jupyter Notebook
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Last pushed
Jul 13, 2022
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