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Privateness Engineering with Alex Watson


Defending your prospects begins with greatest practices for securely capturing, storing, and defending the information you gather for or about them.  When a corporation has a big sufficient dataset, wants sometimes come up for doing analytical workloads or coaching machine studying fashions on this knowledge.  When you use random or mock knowledge to generate a report or prepare a mannequin, you arrive at an output that doesn’t replicate the true use case of the group.  Success on duties like this appears to require manufacturing knowledge.

Alternatively, maybe production-like knowledge is nice sufficient.  On this episode, I interview Alex Watson, co-founder and chief product officer at gretel.  We focus on their answer for privateness preserving artificial knowledge that is still consultant of the underlying dataset.

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