4 Mistakes of Machine Learning Startups

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4 Mistakes of Machine Learning Startups

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You don’t want your startup “to die” from the mistakes of machine learning.
For the past 25 years, I’ve seen thousands of times when a person makes errors — but never when a machine makes a mistake.
Today, a blunder in the learning projects can cost companies millions and several years of useless work.
For this reason, the most common errors in machine learning related to data, metrics, validation, and technology are collected here.
Data.
Chances to make a mistake working with data are rather high.
It is easier to successfully pass a minefield than not to make a mistake while working with the data set.
Unprocessed data .
Unprocessed data is rubbish that will not allow you to be confident about the adequacy of the constructed model.
Therefore, only pre-processed data should be the basis of any AI project.
. Unprocessed data is rubbish that will not allow you to be confident about the adequacy of the constructed model.
Therefore, only pre-processed data should be the basis of any AI project.
To check data on deviations and anomalies and get rid of them.
Getting rid of errors is one of the priorities of every machine learning project.
. To check data on deviations and anomalies and get rid of them.
Getting rid of errors is one of the priorities of every machine learning project.
Lack of data .
Lots of data.
Sometimes limiting the amount of data is the only correct solution.
Accuracy is an essential metric in machine learning.
The model of an AI project also learns from a specific data set.
However, the project won’t handle an attempt to check the quality of the model on the same data set.
The choice of technology in an AI project is still a common mistake, leads if not to fatal, but serious consequences that influence the efficiency and time of the project deadline.
No wonder, you can hardly find a more hyped theme in machine learning than neural networks, due to its suitable-to-any-task universal algorithm

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