A good hybrid approach is to start small with synthetic data to get the project moving initially, but plan to mix in real, anonymized data from pilot users as quickly as possible. This allows the model to learn actual patterns while maintaining development momentum, ensuring the system evolves to handle real-world complexities rather than remaining optimized for fictional scenarios.
A good hybrid approach is to start small with synthetic data to get the project moving initially, but plan to mix in real, anonymized data from pilot users as quickly as possible. This allows the model to learn actual patterns while maintaining development momentum, ensuring the system evolves to handle real-world complexities rather than remaining optimized for fictional scenarios.
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A good hybrid approach is to start small with synthetic data to get the project moving initially, but plan to mix in real, anonymized data from pilot users as quickly as possible. This allows the model to learn actual patterns while maintaining development momentum, ensuring the system evolves to handle real-world complexities rather than remaining optimized for fictional scenarios.
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