First published on MSDN on May 25, 2018
Microsoft Machine Learning Server’s operationalization feature enables data scientists to operationalize their R and Python analytics. In this blog, we will see how to operationalize Keras models as web services in R and Python.
Click
here
to know more about Microsoft Machine Learning Server Operationalization. You can configure Machine Learning Server to operationalize analytics on a single machine (
One-box
).
Create a web service for Keras models in Python
Before you can use the web service management functions in the azureml-model-management-sdk Python package, you must:
-
Have access to a Python-enabled instance of Machine Learning Server that was
properly configured
to host web services.
-
Authenticate with Machine Learning Server in Python as described in "Connecting to Machine Learning Server."
-
Have
Keras
,
Tensorflow
, and
keras-pickle-wrapper
installed.
-
Have a trained Keras model.
Here is a sample python code to create a simple WebService, publish it, and generate swagger. The below sample uses the Keras model to recognize handwritten digits from the
MNIST
dataset.
For fast web service connections in Python, you can create sessions and load dependencies in advance by using
dedicated session pool
.
Create a web service for Keras models in R
-
Have access to a Machine Learning Server instance that was
properly configured
to host web services.
-
Authenticate with Machine Learning Server using the remoteLogin() or remoteLoginAAD() functions in the mrsdeploy package.
-
Install Keras and Tensorflow backend described
here
.
-
Have a trained Keras model.
Here is a sample R code to create a simple WebService, publish it, and generate swagger. The below sample uses the Keras model to recognize handwritten digits from the
MNIST
dataset.
For fast web service connections in R, you can create sessions and load dependencies in advance by using
dedicated session pool
.
REFERENCES
https://keras.io/
https://keras.rstudio.com/
https://www.tensorflow.org/
http://yann.lecun.com/exdb/mnist/
https://pypi.org/project/keras-pickle-wrapper/
https://tensorflow.rstudio.com/keras/reference/install_keras.html
https://docs.microsoft.com/en-us/machine-learning-server/