When, past the age of 40, I started to take an interest in the world of AI and machine learning, I had no intention of ending up dedicating myself to it professionally. However, over time, I’ve noticed that many professionals who have spent part of their working lives in scientific research within an academic setting eventually find themselves knocking on the door of the machine learning world. In my opinion, this has a very simple explanation: pursuing a Ph.D. in a scientific field necessarily involves working with data and analyzing it. Making the leap into this new world simply requires learning new technologies and tools, but the seed is there from the very beginning.
When I started exploring this field, I observed with some enthusiasm that the knowledge I was acquiring was applicable to many areas that were new to me. I currently work in the automotive sector, but I have developed tools in the fields of finance and, of course, sports. Deep Soccer is an experimental tool that I developed mainly as a personal project, for learning purposes. However, when I put the tool online and uploaded a couple of videos to YouTube showcasing how it worked, I was surprised. Most of the people who wrote to me were interested in using the tool in the world of sports betting.
From then on, it’s been a constant learning journey—not just in terms of machine learning but also in software development, cloud resource management, CI/CD methods, marketing, and SEO.
That’s why I always encourage junior software developers or data scientists to roll up their sleeves and start working on their own projects. You don’t need a brilliant idea that will revolutionize any field—take that pressure off your shoulders. It doesn’t even have to be an online tool. Simply having a goal and tackling it with just your knowledge will help you progress much faster than any online course or tutorial.
Best regards,
Miguel
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