Data Quality

Data Quality in the Training Stage of ML Pipelines

Data quality refers to the degree to which a dataset meets the following key requirements for reliable machine learning: Accuracy: The correctness and precision of data values in representing real-world facts Example: Temperature readings from sensors match actual temperatures within acceptable margins. Temperature in Kashmir in January cannot read 30 degrees celsius. Completeness: The presence of all necessary data points and features Example: No missing values in crucial fields. Consistency: The uniformity of data across the dataset Example: Consistent date...

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Ensuring Data Quality in ML Pipelines using DVC

Ensuring data quality is crucial for building reliable ML models. In this guide, we integrate data quality checks into an ML pipeline using DVC (Data Version Control) to enable versioning, tracking, and automated validation of datasets before model training.Machine Learning pipelines automate the end-to-end ML process, handling everything from data ingestion to model deployment. They help break down complex workflows into manageable stages.Important stages of the pipeline:Data Quality checkData preprocessingFeature engineeringModel training and evaluationModel serving and monitoringThis article focuses on...

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