Published by FirstAlign
In this blog we are going to use python code to perform sentiment analysis using the web service we created in Web Services (Part 1) – Create and deploy.
In my last blog we took a dataset and created a Machine Learning model to predict the sentiment of the text through Sentiment Analysis. We used Azure Machine Learning studio for this purpose, partly due to its drag and drop capability. After creating, we evaluated the model and determined both the accuracy and other evaluation parameters.
We created the web service using the Machine Learning Studio with a simple step-by-step process. We were able to test that web service within the interface of Azure ML Studio.
Now the goal is consume that web service out of the Azure ML Studio Interface. To do this we are going to call that web service using python on our local machine.
What is Web Service?
A Web service is a piece of software that allows itself to be accessed over the internet using some standards such as SOAP (Simple Object Access Protocol). Here in our use case we are going to use RESTful web Service, RESTful webservice is a lightweight scalable service, the API in RESTful webservice are exposed in uniform, secure and stateless way. Here we have created a RESTful web service when we will invoke it with text parameters it will return us the sentiment of the text.
As we have a deployed a web service using Azure ML studio in “Web Services (Part 1) – Create and deploy” your first web service, now its time to consume it from the outside i.e. our local machine or some other virtual machine (VM). To achieve this we are using Python. So lets start.
In the last blog we left while we had a working web service end point, created using Azure ML studio.
Now click on the New Web Services Experience and you will be redirected to the page as shown below;
Click on consume, you will get all the basic items required for consuming the web service.
In the figure above you can see we have a primary key, secondary key and a URL for the prediction of both batch requests and simple single request response. We will use this in our code to perform the prediction.
On the Consume page you can see the sample code in different languages. We will use the python code and get the results.
Here we will specify the way data is sent to make the prediction.
Specify URL and API key
Copy the URL key and paste it here in this snippet.
Specify Request and Header
Here in this snippet, headers are created for the request. This headers api key and body are then used to create the request object.
Try except block:
Here is this snippet. Try catch block is used to make request and deal with exceptions where/ if they occur.
Here is the full code.
Run the code
As you run the code you will get a json response. The most important feature to look for is ‘scored labels’ which will show the prediction. Here it is -1 based on the type of text supplied.
This blog is an extension of the previous blog where we created a sentiment analysis web service. We have taken this further in this blog by consuming the web service using python. Results were provided in JSON which can now be utilized in a any way we want.
For more information on consuming web service check the following links.
Consume web service – ML Studio (classic) – Azure
Once you deploy an Azure Machine Learning Studio (classic) predictive model as a Web service, you can use a REST API to…www.google.com
Deployment and consumption – ML Studio (classic) – Azure
You can use Azure Machine Learning Studio (classic) to deploy machine learning workflows and models as web services…www.google.com
In the next blog we will look at how we can scale the Web Service so stay tuned until than happy coding ❤.