I am looking for a solution that allows me to host my trained Sklearn model (that I am satisfied with) on SageMaker without having to retrain it before deploying to an endpoint.

On the one hand I have seen specific examples for bring-your-own scikit model that involve containerizing the trained model but - these guides go through the training step and dont specifically show how you can alternatively avoid retraining the model and just deploy. (https://github.com/awslabs/amazon-sagemaker-examples/blob/27d3aeb9166a4d4dbbb0721d381329e41d431078/advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb)

On the other hand, there are guides that show you how to BYOM only for deploying - but these are specific to MXNet and TensorFlow frameworks. I noticed that the way you export your model artifacts among frameworks differs. I need something specific to Sklearn and how to get to a good point where I have model artifacts in the correct format Sagemaker expects(https://github.com/awslabs/amazon-sagemaker-examples/tree/27d3aeb9166a4d4dbbb0721d381329e41d431078/advanced_functionality/mxnet_mnist_byom)

The closest guide I have seen that might work is this one: https://aws.amazon.com/blogs/machine-learning/bring-your-own-pre-trained-mxnet-or-tensorflow-models-into-amazon-sagemaker/

However, I dont know what my sklearn "model artifacts" includes. I think I need a clear understanding of what sklearn model artifacts looks like and what it includes.

Any help is appreciated. The goal is to avoid training in Sagemaker and only deploy my already trained scikit model to an endpoint.