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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Directly connecting jupyter notebook to sagemaker for end-point inference\nUsing AWS -Sagemaker to train Machine learing model directly from jupyter notebook\n\n### Most of the times we use AWS UI to access , open notebook inside sagemaker , train the model and inference from endpoint but what if we configure sagemaker using CLI and access it from jupyter notebook from your local machine\n\n\u003eInshort we are just using AWS UI for creating IAM role , S3 bucket and the best part is we don't have to use sagemaker UI for training and inferencing from end-point\n\n## Steps : \n\n### Configure AWS CLI \u0026 IAM role\n\n- First create IAM role from ur AWS , set poicy access as admin or sagemaker access and create access key\n- Download and Install Amazon CLI and configure\n\u003e aws configure\n- paste the access \u0026 secret key\n-Follow the code in the notebook\n\n### Create S3 bucket \u0026 custom script\n\nNotebook :  AWS_Sagenaker_mL_train.ipynb [https://github.com/mahikshith/AWS_Sagemaker_ML_trainer/blob/main/AWS_Sagenaker_mL_train.ipynb]\n\n- Create S3 bucket and upload the data to S3 using the code in the notebook\n- Write a custom script [sagemaker_script_ml.py] which contains code for sagemaker to train the model\n- Create an entry point so that AWS sagemaker picks the code\n- Create a new role for sagemaker service- execution and copy \"ARN\" and assign to role variable in the notebook\n- Enter all the hyper-parameters for the endpoint and RUN the job\n\n### Model -training and inferencing : \n\n- When we run the job , the model training starts under training-jobs\n- Now create an endpoint for inferencing\n- Stop or delete the end-point once the inference is 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