Unlike typical IT projects, machine learning projects are highly innovative and cutting-edge, and as a result, much riskier. Here, being successful largely depends on whether you know what your ultimate goal is and consider your chances to achieve it.
Understanding the idea behind your ML project and the essential steps you need to take to make it work will help you avoid more than just disappointment. It will also help you save time and money you may waste if you don’t take some major risks into account.
Today we sat down with Krzystof Sopyła, Head of Machine Learning and Data Engineering at STX Next to answer questions about the main risks related to ML projects:
Read the article to learn the tips from our experienced ML manager that will help you identify potential shortcomings of your ML project and address them properly before you even start the project!