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The problem I wanted to solve was to fully automate the process of generating profile images of football players for several of the company's games. The system is used in the mobile game Golden Boot 2019, available in both iOS and Android, and with over one million installs since its release.

I built a pipeline of three models, each applied sequentially. First, a cutting-edge GAN network retrained to my own dataset that generates realistically looking football player faces. Second, a logistic classifier built from the last layer of a VGG network, to classify the output of the GAN into "good" faces and "bad" faces, ensuring that only images of sufficient quality are displayed to the user. Third, another logistic regression on top of the last layer of a VGG net to classify the face according to its ethnicity. This last step was necessary in order to have control over the correlation between football player nationality and his physical appearance.

A natural language processing system to automatically classify customer issues. The tool was developed during my time at Nordeus, a mobile gaming company with over two million daily active users. The end-goal was to reduce the number of tickets that human agents had to process, and increase customer satisfaction overall.

A suite of machine learning models to quantify and explain the valuations of banking institutions in the Gulf region. I was the machine learning engineer in charge of designing and implementing such a system according to the data availability and end goals of the client.

Lola Market is a Spanish startup that lets you order groceries online at your favorite shop and get them delivered to your home. The company has a fleet of shoppers that go to the stores, do the shopping, and take it to the user's house.

A big question for Lola's operations team was: "how many shoppers should be available at each location and hour of the day to guarantee 100% availability to our users and to minimize shopper idle hours?". The goal of the project was to automate and improve the allocation of shoppers in geographies and timeslot.

The solution I developed is a machine learning (ML) model that predicts future user demand at each geography (city, district) and hour of the day for the following two weeks. I also developed a suite of Tableau dashboards to make the system transparent to Lola's Operations team.

Created and published an online course on reinforcement learning (RL), from fundamentals to cutting-edge deep RL.

The course is available online for free.

The goal is to teach my students, with a hands-on approach, how to implement the key RL algorithms from scratch using Python and PyTorch.

A hands-on course to learn how to build and deploy large language model (LLM)-powered applications.

In this tutorial, you will design, build, and deploy a financial advisor using LLMs and MLOps best practices. This hands-on tutorial will help you go beyond LangChain demos in Jupyter notebooks and build real-world machine learning (ML) products using LLMs.

A live, interactive course in which we (you and me) will build a modular real-time machine learning (ML) system to predict short-term crypto prices.

This course is ideal for those who are familiar with Python and want to learn how to build modular ML systems that transform raw data into predictions in real time.