teach me AWS sagemaker project from 0 till end

teaching instructions how to do AWS sagemaker python logistic regression ML project from 0 to end with production for 20 predictions per second
you provide video or doc/pdf file with screen copies how to do
1
train scikit learn logistic regression ML project (https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html )
use this data and code as base example
https://scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py
2
hyperparameters tuning (some python library like optuna ) on many distributed workers in parallel
3
inference model for production
4
how to use inference model for production

main idea : I need to learn how to do ML project on sagemaker for personal use

also teach how to create test code and environment -> should be created to request these 20 predictions per second , possibly from local computer or better from another cloud service like azure , digital ocean and etc..

2 days timeline

I need to learn only sagemaker - > I am familiar with elementary python and basic machine learning models

you deliver
1
video or doc file with screen copies : step by step how to implement such a project
2
python code

some ideas
https://www.youtube.com/watch?v=Le-A72NjaWs

https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker-python-sdk/scikit_learn_iris/scikit_learn_estimator_example_with_batch_transform_outputs.ipynb

https://github.com/aws/amazon-sagemaker-examples/tree/main



next 2 project will be to do the same for GCP and Azure