How to deploy a Pre-Trained model using AWS SageMaker Notebook Instance?

问题

I have a pre-trained model which I am loading in AWS SageMaker Notebook Instance from S3 Bucket and upon providing a test image for prediction from S3 bucket it gives me the accurate results as required. I want to deploy it so that I can have an endpoint which I can further integrate with AWS Lambda Function and AWS API GateWay so that I can use the model with real time application. Any idea how can I deploy the model from AWS Sagemaker Notebook Instance and get its endpoint? Code inside the .ipynb file is given below for reference.

import boto3
import pandas as pd
import sagemaker
#from sagemaker import get_execution_role
from skimage.io import imread
from skimage.transform import resize
import numpy as np
from keras.models import load_model
import os
import time
import json
#role = get_execution_role()
role = sagemaker.get_execution_role()

bucketname = 'bucket' # bucket where the model is hosted
filename = 'test_model.h5' # name of the model
s3 = boto3.resource('s3')
image= s3.Bucket(bucketname).download_file(filename, 'test_model_new.h5')
model= 'test_model_new.h5'

model = load_model(model)

bucketname = 'bucket' # name of the bucket where the test image is hosted
filename = 'folder/image.png' # prefix
s3 = boto3.resource('s3')
file= s3.Bucket(bucketname).download_file(filename, 'image.png')
file_name='image.png'

test=np.array([resize(imread(file_name), (137, 310, 3))])

test_predict = model.predict(test)

print ((test_predict > 0.5).astype(np.int))

来源：https://stackoverflow.com/questions/65434323/how-to-deploy-a-pre-trained-model-using-aws-sagemaker-notebook-instance