What are some effective techniques for ensuring data quality in AI?
There are several techniques to ensure data quality in AI, including data validation, cleansing, and enrichment.
Can you explain data validation in more detail?
Data validation involves checking the accuracy and quality of data before it's used in AI models. This can include format checks, range checks, and consistency checks.
What is data cleansing and why is it important?
Data cleansing is the process of correcting or removing inaccurate records from a dataset. It's crucial because poor quality data can lead to misleading results in AI.
How does data enrichment improve data quality?
Data enrichment enhances existing data by adding relevant information from external sources, which can improve the context and accuracy of the data.
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