Tiny

1. Introduction: A technical choice that became a business decision Not every data problem is an invitation for Deep Learning. And not every Machine Learning model is easy to sustain at scale. This is because Machine Learning (ML) and Deep Learning (DL) have structural differences that go beyond the algorithm. Therefore, deciding between an approach […]

1. The cloud accelerated everything, including invisible errors. The cloud promised agility, scale, and efficiency, and it delivered. The problem is that, in this movement, many companies accelerated more than they realized. With each new integration, automation, or API layer, data multiplies, transforms, and exists in places not always […]

Introduction: Why controlling invisible data has become a priority. There's something that goes unnoticed in many companies: the illusion that migrating data to the cloud is the same as controlling it. In practice, what grows with the cloud is not only processing capacity, but also the complexity of understanding where the data is, […]