Perhaps this is the first time you've read about it. But we assure you that you interact with it almost daily. What do we mean?

Well, I'm sure you get recommendations from YouTube, Netflix or Spotify on a regular basis. And probably more than once they are good recommendations. Maybe you also ask Siri or Alexa something and they answer your questions. Well, behind all this, there is machine learning.

Simply put, this is a branch of Artificial Intelligence (AI) that is responsible for converting large amounts of data into computer programs, capable of drawing inferences and providing solutions in response. This is a fundamental skill for technology to identify patterns and make increasingly accurate predictions. .

The Amazon success story

More than a decade ago, Amazon set out to make machine learning a philosophy that cuts across the company. To achieve this, it needed to invest resources, time and effort, and get all its members involved in this challenge. How did they do it?

In the first instance, the management team asked each team leader to think about how AI could enhance or bring benefits to their specific area of work .

Simultaneously, they invited subject matter experts to collaborate on medium- and long-term AI initiatives. They hired data scientists externally and created the Machine Learning University to train their developers.

In this way, they created the Amazon SageMaker platform, which allows them to create, train and deploy machine learning models in the cloud. This tool simplifies the creation of models for developers, making it a more accessible and scalable task.

Thanks to these measures, all of Amazon's departments were in one way or another affected by machine learning. The AI systems that provide suggestions and offer recommendations to its customers improved exponentially over time.

Amazon can estimate demand for a huge inventory of products. In addition, the sophistication and quality of its predictive models allow the company to meet users' expectations, providing convenience and speed in each shipment. As well as designing more personalized experiencesthat add value and build customer loyalty.

Consolidating as a benchmark in machine learning is not something that is achieved overnight. As we have seen, they were years of investment in technology and research, as well as years of cultural transformation, encouraging their teams to include innovation as a key value, to experiment, develop tolerance to mistakes and get to the desired place.