As an applied ML company that seeks to make some sense out of financial data, we at BBVA Data & Analytics enjoy sharing articles, blogs and news about Artificial Intelligence (AI) and Machine Learning (ML). Two years ago we created a G community to share the latest on Machine Learning technologies that we find interesting and stimulating. This experiment has continued to grow into a thriving site for knowledge sharing and debate around the discipline and its latest developments. As we enter 2018, we'd like to share the most popular and discussed posts from our blog in 2017. It reflects what we believe are valuable insights for the company.

I come from physics and computer engineering. I studied both in Venezuela, and then I did a Master in Physics in Mexico. But I consider myself a Data Scientist. So even though I have a good and extensive background in math, calculus and statistics, it was not easy to get started with machine learning and then deep learning. This subjects are not new, but the way we study them, how we build software and solutions that use them, and also the way we program or interact with them has changed dramatically.

In another talk, Piotr Migdał gave us some tricks and tips about reproducibility and a number of best practices on team cooperation and model deployment. It's important to be aware that even a single untracked parameter tweak can lead to frustration and inefficiencies in the whole team. I can't resist to mention his good humor with the parallelism between Borja's Ecce Homo painting and model reproducibility issues.