Proofs of Concepts that end up gathering dust on shelves generate big expenses with no ROI. We refuse to sell shiny prototypes that take too much distance from the real world. Any project that does not end up in a production environment has missed its target. That does not mean that all projects will succeed in this quest, but it must be the primary focus when launching a new project.
We specialize in MLOps – the methodologies,processes and tools to industrialize Machine Learning projects.
What big tech companies do with their data is a pure fantasy for most companies. Those tech giants have been built from Day 1 with deep Data foundations, and all of their resources – humans, but not only – are centered on this exact topic. This is not the case for the vast majority of other corporations. Therefore, their publications can be seen as an inspiration, but you can’t expect to achieve the same results the next day.
First things first, it is essential to start developing a culture of Data and to build a strategic plan that does not skip the initial steps. The first projects must be realistic, with quick and visible results to keep people motivated.
Data Science is not Business Intelligence. In the past, Business Intelligence was handled by IT departments with siloed data sources and a single gateway to other business units. New organizations requires data itself and data skills to be distributed and transmitted fluidly between stakeholders. Data is everywhere. Everybody needs it, everybody uses it.