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For example, when customers log onto our website or mobile app, our conversationalAI capabilities can help find the information they may want. How are you looking at model evaluation for cases where data adapts rapidly? The same applies for buying a car with Capital One. KM: Final question before we end the session.
For example, when customers log onto our website or mobile app, our conversationalAI capabilities can help find the information they may want. How are you looking at model evaluation for cases where data adapts rapidly? The same applies for buying a car with Capital One. KM: Final question before we end the session.
Adaptability over time To use Text2SQL in a durable way, you need to adapt to datadrift, i. the changing distribution of the data to which the model is applied. For example, let’s assume that the data used for initial fine-tuning reflects the simple querying behaviour of users when they start using the BI system.
For instance, if you’re interested in a problem like testing a conversationalAI solution or detecting out-of-distribution data, your ability to do that and reason about it is determined by your ability to have a great dataset. Peter Mattson: I think the rate of datadrift is highly problem sensitive.
For instance, if you’re interested in a problem like testing a conversationalAI solution or detecting out-of-distribution data, your ability to do that and reason about it is determined by your ability to have a great dataset. Peter Mattson: I think the rate of datadrift is highly problem sensitive.
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