Source: thenewstack.io
Why So Many Organizations’ Artificial Intelligence Initiatives Fail
You’re looking at your iPhone for a particular picture of your friend, taken a couple of years ago. Within seconds you find the picture you’re looking for.
It can be boiled down to three big hurdles: the wrong data in the wrong infrastructure at the wrong time.
New feature engineering — processing data necessary to choose and train models — requires going back to the raw data for different aggregations.
The quality of the models, and their outcomes, increases with the volume of event data ingested.
It can be boiled down to three big hurdles: the wrong data in the wrong infrastructure at the wrong time.
New feature engineering — processing data necessary to choose and train models — requires going back to the raw data for different aggregations.
The quality of the models, and their outcomes, increases with the volume of event data ingested.
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