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Machine learning lifecycle

Dataconomy

Preparing data Once data is collected, the next step is preparing it for processing, shaping it into a format suitable for machine learning models. Data classification: Understanding the characteristics and quality of the data is vital for identifying trends and anomalies.

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Using Snowflake Data as an Insurance Company

phData

Masked data provides a cost-effective way to help test if a system or design will perform as expected in real-life scenarios. As the insurance industry continues to generate a wider range and volume of data, it becomes more challenging to manage data classification.