Here is an example of deploying an sklearn model using ClearML serving.

However, please note that sklearn-like models don't have input and output shapes in the same sense as deep learning models have. Setting the I/O shapes using the CLI is usually meant for GPU-based deep learning models that need to know the sizes for better GPU allocation. In the case of sklearn on CPU, all you have to do is set up your preprocessing.py script such that the data coming in is what the model.predict() expects.