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But most important of all, the assumed dormant value in the unstructured data is a question mark, which can only be answered after these sophisticated techniques have been applied. Therefore, there is a need to being able to analyze and extract value from the data economically and flexibly.
For instance, they could fail to embed fundamental capabilities like accurate grammar in NLP systems or cover up systemic flaws like societal prejudices. The standard testing method involves calculating an overall performance metric on a subset of the data. Zeno is made available to the public via a Python script.
Generally, data is produced by one team, and then for that to be discoverable and useful for another team, it can be a daunting task for most organizations. Even larger, more established organizations struggle with datadiscovery and usage. Robert, you can go first. I have a certain point of view on this.
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