In this article, we will not discuss developing Machine Learning model but rather containerizing our ready to deploy ML API (Flask) with the help of Docker so that there is no hassle that our model is not working in the production environment or while deploying it on cloud or simply sharing the working model to friends and colleagues.

Note: Docker is kind of useful as well as in trend nowadays so better start containerizing your models.

Firstly, I assume that if you are reading this article you may be already familiar with the Docker. If not, don’t worry I’m still going to define that in my own layman’s terms.

#tutorial #machine learning #docker #artificial intelligence #deep learning #mlops #containerize