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Every day I open my email inbox and I find dozens of emails with enlightening articles on Artificial Intelligence. I am always stunned by the sureness of the predictions envisioned in those articles. It seems that they know exactly where and when the next transformation will happen. Trends, sectors, methodologies, industries, clients needs. It seems that everything has been visualized and this is like planning for the next development phase.

Is it like this? I don’t think so. I believe that no one has a clue and yet many people have a clear view of the future.

And why is…


Why hiring the right people in Machine Learning is not enough.

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Joe has recently been hired as a Machine Learning Engineer at Reproducible Inc, a software house that uses Machine Learning for various activities. It was Francine’s idea, the Head of Data. She thought that this could increase the productivity of Machine Learning models and their lifetime in production.

In fact, Francine has spent the last three months answering to her boss, trying to explain the reason behind the mistrust of Machine Learning within the company. …


Or you will die as an amateur in the corporate world

Thanks to Ben White for sharing their work on Unsplash.

In a super-connected world, sometimes I feel overwhelmed by the proliferation of communication tools and channels I have on my laptop and smartphone (not to mention the smartwatch). Shall I send an informal WhatsApp or just call (yes, the smartphones can still place simple calls)? On occasion, I ask myself if this hyper-connected world we live in simplifies our corporate life.

When I started working back in 2000, I had Outlook, Bloomberg chat because I worked in the financial industry, the mobile phone just to place calls and send…


And what you should not pretend

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There is been a lot of confusion around Data Science in the last years, and often Data Scientists have been depicted in a super sexy way, which did not reflect the reality.

Even in terms of objectives, the managers have created expectations that are not in line with the skills and capabilities requested to Data Scientists. They included proper software coding, architecture design, database management, which are not part of Data Scientists’ background. At least not every Data Scientist.

On the other hand, Data Scientists have failed the approach on their own. They have…


And make your customers happy too.

Thanks to Lukas Blazek for sharing their work on Unsplash.

Learning Process

The process of selling products is not only dependent on its quality, although it is very important, but on the timing that those products are served to interested customers. The time is of the essence, as it is the the behaviour of users, meaning the actions they have performed in the past and they are performing right now, to be identified in a potential buying mood.

This is very important, because there is nothing more annoying that the right recommendations at the wrong time or the other way around. …


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Not too short to castrate their creativity, but short enough to preserve your production environments, and — ultimately — your organization.

Data Science can be a powerful resource for any organization and their chances to beat the competition. In a world driven by data, being able to read the changes through the analysis of data is a big advantage. Companies can intercept the customers, retain them, offer products and services in line with their interests.

However, Data Scientists have a very open-mind and they tend to unlock too much their creativity, looking for patterns where they are not, for insights…


One of the key roles of recommender systems is to push customers spending more and more. You open your browser, activate your smartphone, walk into a shop (if Covid-19 allows it) and you are bombarded with messages, pop-ups, offers, last second deals, related items, things will surprise you.

It is truly necessary? Is this important to both guarantee profits and the rigth products to show, on top of preserving the customers peacefulness?

What we propose is a way to accommodate a genuine request from clients to purchase without the hassle of pissing him off. That’s because, let’s face it. …


Photo by Clarisse Croset on Unsplash

Governing the Machine Learning lifecycle

Machine Learning models are now widely used at many levels in every organizations. They are implemented for recommending products to buy, recognize images, detect frauds, and many other cool stuff.

Up until now, the approach has been very naïve, pretending that Data Science and Machine Learning are different from any other Software development process and that Data Scientists have some sort of gray area where anything is admitted.

Finally, we have now reached a point where this is not true anymore. …

Giancarlo Cobino

Quant portfolio manager in the past. Now Machine Learning enthusiast, focused on the whole lifecycle of ML projects. Insights with control!

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