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Code me a treasure-hunting game.
Cover Gangnam Style by Psy in the style of Adele.
We know, of course, that it isnt magic.
Only then can they make accurate predictions, perform important tasks and improve over time.
This means that the quality of information we input into our tools is crucial.
So, how can we double-check we foster quality data to build practical, successful AI models?
Lets take a look.
COO of Northern Data Group.
Its the backbone behind effective decision-making, strong operational processes and, in this case, valuable AI outputs.
Yet maintaining good qualitydatais tough.
Poor data often also contains limited and incomplete information that fails to accurately reflect the wider world.
Returning to AI, messy or biased data can have a similarly catastrophic effect on a modelsproductivity.
These numbers must change if data is to power successful AI outcomes worldwide.
Most importantly, we need to continuously check and update the data to maintain its quality.
Next, engineers must implement a strong data governance framework that incorporates regular data quality assessments.
Data scientists should continuously detect and correct inconsistencies, errors, and missing values within datasets.
This tracking ensures transparency and accountability in the case of a bad outcome.
And its becoming particularly crucial in the face of greater concerns about AIs privacy.
A good option is to anonymize all the data you collect.
That way, you’re able to reuse it in further AI model training without compromising customer privacy.
And data residency is a growing focus across the globe.
In Europe, for example, GDPR stipulates that EU citizens data must reside in the European Economic Area.
It isnt quite magic but the results can be equally as impressive.
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