The artificial intelligence field is strongly connected with Big Data technologies. One of its areas deals with the study of natural language. Computers can be taught to recognize certain patterns in the processed texts and, based on them, they can automatically classify sentences, phrases or even full documents in predefined groups. Using open source instruments, one can easily configure such a project, capable of classifying text, based on a prior automatic learning phase, using a preset input data.

How managing large projects differs from the projects that are spun up in general in IT companies. There are 5 angles that need to be considered when talking about different project sizes and we are going to dive in each of them. I will gladly share the challenges I've met while leading a large project for the first time. The project I refer to is a migration of 300 components to a new platform, which spans over 16 months, which involves 30+ development teams, and which costs >9 M.

Besides the official Docker documentation, there are several good sources on the Internet where you can read about how to kick-start your dockerized environment on your project, including Stack Overflow Docker tag, official Docker images for MySQL, and Postgres. Unfortunately, there aren't so many places where one can read about the failures in setting up Docker in various common use cases. You may wonder "why would anyone be interested in such a thing?" I think the value of such stories lies in the fact that we discover how such solutions work by trial and error, many times - pretty much the same method applied by a 2nd grader. Do you find yourself in a similar situation? You probably do, so the ideas from here will bear some value to you.