From the course: Database Design Fundamentals
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Considerations for AI: Unstructured data and analytics
From the course: Database Design Fundamentals
Considerations for AI: Unstructured data and analytics
- [Instructor] Data quality is one of the most important considerations to keep in mind at all times when working in data. Ensure mechanisms are in place to clean and pre-process data before it's ingested into the database. High quality, consistent data is crucial for effective AI model training. Implement caching mechanisms to reduce the load on the database during intensive AI processing tasks, especially for read heavy operations whenever possible. This is a performance consideration that will support consistent throughput to the GPUs. Implement data versioning strategies to track changes to data sets over time, which is crucial for reproducibility in AI experiments. Get to know your data. Almost every aspect of architecting, designing, and performance tuning data is easier if you know every aspect of your data. This means how it is used, how it is stored, the quantitative, as well as the qualitative aspects of it.
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