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Reasons _behind_ no_ shortcuts_ to_ machine _learning

Reasons behind no shortcuts to machine learning

Reasons _behind_ no_ shortcuts_ to_ machine _learningCompanies need to understand that to obtain good data science will need a lot of time investment in an enterprise, and eventually gives the people a room to learn and grow. By understanding this simple concept they will not run behind shortcuts for machine learning
Big data remains a game for just 1-15 per cent as suggested by new O’Reilly survey data. According to this survey, most enterprises up to 85 per cent have still not cracked the code on AI and machine learning. With just a mere of 15 per cent of sophisticate enterprises running such models in production for more than five years, it is high time the rest understand this. Basically, these companies tend to give a larger amount of time and attention to critical areas like model bias and data privacy, whereas comparative startups still are pertaining to find the On button.
Unfortunate thing is that those companies hope to bridge the data science gap with automated shortcuts like Google’s AutoML or using paid consultants where the answer seems to be getting impossible day by day.
One interesting data point emerging from the survey is regarding the speech of such about themselves and Companies with extensive data experience refer to data science spade as data science spade. With no one looking to the cloud machine learning services at least till the recent times, will companies with less than two years into production ever experiment on external consultants to build their machine learning models is a big dilemma. This may look like an opportunity for companies to easily benefit from data science without making any investment in people, but I call it a fools-gold approach.
Let’s just conclude by saying the more sophisticated the company is with data, the higher its data science team is; both build the models as well as evaluates key metrics for a project’s success.

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