Simplify Your Machine Learning Projects

Many businesses are eager to adopt machine learning to improve their products and services. However, many data scientists get too caught up in creating the perfect model and using state of the art techniques. By doing this, they forget the most important thing: delivering a functional minimum viable product (MVP). In this post, I’ll discuss three reasons why it’s better to focus on getting a working MVP first before spending too much time on creating a complex model. To end the post, I give three tips for creating a MVP. When I started as a data scientist around six years ago, I wasn’t really interested in topics like Naive Bayes, linear regression, and statistics. Maybe it was because of my mathematical background, and I had already learned these topics during my studies. Instead, I was way more interested in neural networks, language models, computer vision, and reinforcement learning. These topics intrigued me, and I took courses to learn them as quickly as possible. When I had to deal with real business problems at companies, I always tried complex models and solutions that I found fun to work on, often involving deep learning, datasets scraped from the web, and complex architecture. Unfortunately, my code was messy and hard to read. I remember one project that I spent months working on. I had weekly meetings with the business, but I was the one who was talking, and the end result was too complex and hardly used. Important takeaways from that period are to not overcomplicate machine learning solutions and to talk less. With this post, I hope I can prevent you from making the same mistakes I made and explain what to do instead. <a href="https://towardsdatascience.com/simplify-your-machine-learning-projects-ab171d19c9ef">Website</a>