If you are implementing machine learning model with Amazon SageMaker, obviously you would want to know how to access trained model from the outside. There is good article posted on AWS Machine Learning Blog related to this topic - Call an Amazon SageMaker model endpoint using Amazon API Gateway and AWS Lambda. I went through described steps and implemented REST API for my own module. I went one step further and tested API call from JavaScript application implemented with Oracle JET JavaScript free and open source toolkit.
I will not go deep into machine learning part in this post. I will focus exclusively on AWS SageMaker endpoint. I'm using Jupyter notebook from Chapter 2 of this book - Machine Learning for Business. At the end of the notebook, when machine learning model is created, we initialize AWS endpoint (name: order-approval). Think about it as about some sort of access point. Through this endpoint we can call prediction function:
Wait around 5 minutes until endpoint starts. Then you should see endpoint entry in SageMaker:
How to expose endpoint to be accessible outside? Through AWS Lambda and AWS API Gateway.
AWS Lambda
Go to AWS Lambda service and create new function. I already have function, with Python 3.6 set for runtime. AWS Lambda acts as proxy function between endpoint and API. This is the place where we can prepare input data and parse response, before returning it to API:
Function must be granted role to access SageMaker resources:
This is function implementation. Endpoint name is moved out into environment variable. Function gets input, calls SageMaker endpoint and does some minimal processing for the response:
We can test lambda function and provide test payload. This is test payload I'm using. This is encoded list of parameters for machine learning model. Parameters describe purchase order. Model decides if manual approval is required or not. Decision rule - if PO was raised by someone not from IT, but they order IT product - manual approval is required. Read more about it in the book mentioned above. Test payload data:
Run test execution, model responds - manual approval for PO is required:
AWS API Gateway
Final step is to define API Gateway. Client will be calling Lambda function through API:
I have defined REST resource and POST method for API gateway. Client request will go through API call and then will be directed to Lambda function, which will make call for SageMaker prediction based on client input data:
POST method is set to call Lambda function (function with this name was created above):
Once API is deployed, we get URL. Make sure to add REST resource name at the end. From Oracle JET we can use simple JQuery call to execute POST method. Once asynchronous response is received, we display notification message:
Oracle JET displays prediction received from SageMaker - manual review is required for current PO:
Download Oracle JET sample application with AWS SageMaker API call from my GitHub repo.
I will not go deep into machine learning part in this post. I will focus exclusively on AWS SageMaker endpoint. I'm using Jupyter notebook from Chapter 2 of this book - Machine Learning for Business. At the end of the notebook, when machine learning model is created, we initialize AWS endpoint (name: order-approval). Think about it as about some sort of access point. Through this endpoint we can call prediction function:
Wait around 5 minutes until endpoint starts. Then you should see endpoint entry in SageMaker:
How to expose endpoint to be accessible outside? Through AWS Lambda and AWS API Gateway.
AWS Lambda
Go to AWS Lambda service and create new function. I already have function, with Python 3.6 set for runtime. AWS Lambda acts as proxy function between endpoint and API. This is the place where we can prepare input data and parse response, before returning it to API:
Function must be granted role to access SageMaker resources:
This is function implementation. Endpoint name is moved out into environment variable. Function gets input, calls SageMaker endpoint and does some minimal processing for the response:
We can test lambda function and provide test payload. This is test payload I'm using. This is encoded list of parameters for machine learning model. Parameters describe purchase order. Model decides if manual approval is required or not. Decision rule - if PO was raised by someone not from IT, but they order IT product - manual approval is required. Read more about it in the book mentioned above. Test payload data:
Run test execution, model responds - manual approval for PO is required:
AWS API Gateway
Final step is to define API Gateway. Client will be calling Lambda function through API:
I have defined REST resource and POST method for API gateway. Client request will go through API call and then will be directed to Lambda function, which will make call for SageMaker prediction based on client input data:
POST method is set to call Lambda function (function with this name was created above):
Once API is deployed, we get URL. Make sure to add REST resource name at the end. From Oracle JET we can use simple JQuery call to execute POST method. Once asynchronous response is received, we display notification message:
Oracle JET displays prediction received from SageMaker - manual review is required for current PO:
Download Oracle JET sample application with AWS SageMaker API call from my GitHub repo.
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