CODEWITHSUNDEEP
amazon-web-servicesamazon-sagemaker
HarryShotta
HarryShotta

Reputation: 347

Beginners guide to Sagemaker

I have followed an Amazon tutorial for using SageMaker and have used it to create the model in the tutorial (https://aws.amazon.com/getting-started/tutorials/build-train-deploy-machine-learning-model-sagemaker/).

This is my first time using SageMaker, so my question may be stupid.

How do you actually view the model that it has created? I want to be able to see a) the final formula created with the parameters etc. b) graphs of plotted factors etc. as if I was reviewing a GLM for example.

Thanks in advance.

Upvotes: 1

Views: 1260

Answers (1)

julitopower
julitopower

Reputation: 181

If you followed the SageMaker tutorial you must have trained an XGBoost model. SageMaker places the model artifacts in a bucket that you own, check the output S3 location in the AWS SageMaker console.

For more information about XGBoost you can check the AWS SageMaker documentation https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html#xgboost-sample-notebooks and the example notebooks, e.g. https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/xgboost_abalone/xgboost_abalone.ipynb

To consume the XGBoost artifact generated by SageMaker, check out the official documentation, which contains the following code:

# SageMaker XGBoost uses the Python pickle module to serialize/deserialize 
# the model, which can be used for saving/loading the model.
# To use a model trained with SageMaker XGBoost in open source XGBoost
# Use the following Python code:

import pickle as pkl 
model = pkl.load(open(model_file_path, 'rb'))
# prediction with test data
pred = model.predict(dtest)

Upvotes: 1

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