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AI for everyone

From Cloud Services to Data Science

Why use Data Science ?

Collecting data is easy, so easy that almost all companies have far more data than they can analyze. As a general rule, data storage is motivated by backup or regulatory issues,
But above all, it’s the best way to analyse past activity in order to anticipate the future.

Understand the why?
Anticipate in order to reproduce (or not) a past experience or to be alert in case of a similar situation.

The data are so complex, rich and numerous that only the scientific approach can solve these problems.

We call that:  DATA SCIENCE

Understand your data

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Thanks to powerful statistical algorithms in general and occasionally via ML model extractions, we can explain certain phenomena more precisely, correlate them with others and thus detect important information, decisive KPIs or simply explain an event.

Revenue optimization

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Thanks to the statistical approach and more specifically Machine Learning, we can generate insights which analysts and business users can translate into business value.


forecasting situations

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Predictive analytics uses historical data to build forecasts. The better is the quality of the data and the longer they are historically, the better is predictability – Almost all prediction cases are feasible as long as this rule is met !


Anomaly Detection

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Anomaly detection identifies data points, events, and/or observations that deviate from a normal behavior based on the history. Anomalous data can indicate critical incidents, such as a technical glitch, or potential opportunities, for instance a change in consumer behavior.

The unavoidable of unstructured data

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Emails, text files, websites and social networks or media such as photos, videos or audios are unstructured data.
If for tabular data the Machine Learning approach is a technical-statistical evolution, for computer vision, NLP or audio ML is a real REVOLUTION thanks to the Deep Learning approach.
Nowadays, DL is the state of the art, the essential method to treat these subjects.

How the D-process Works ?




AUDIT - Feasibility


Build the right architecture


Leverage data to its full potential

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