PyCaret - the low code machine learning solution - from data preprocessing to REST API and Docker

If you deal with building machine learning algorithms or managing them, you know well that creating a machine learning algorithm is a time-consuming task.

You spend a lot of time with exploratory data analysis, data preparation, experimenting with different machine learning algorithms, optimizing them, and evaluating the results. Later on, you spend time with the deployment of the already selected model, for example, creating and dockerizing a rest api.

PyCaret offers a solution for all of the above. Of course, there are things that PyCaret is not suitable for, it is not good for on its own is model tracking (saving model artifacts, model results, etc.), which is where MLflow can be used and is supported by PyCaret, etc.

But how can you build machine learning models 10x faster? I’ll show you!