Gain insights into building REST APIs with FastAPI, explore parallel processing in Python, and discover how to deploy APIs on Microsoft Azure with a CI/CD pipeline using GitHub.

Build a REST API Using Python and Deploy It to Microsoft Azure.png

fastapi-docker.tar.gz

FastAPI-test

Python3

In this course, you will learn how to build a REST API using a lighter web framework for Python, i.e., FastAPI. You will learn the basics of FastAPI and learn how to implement parallel processing in Python APIs.

Finally, you will deploy your API on Microsoft Azure and build a Continuous Integration/Continuous Development (CI/CD) Pipeline using Github.

Build a REST API Using Python and Deploy it to Microsoft Azure

# Perform sentiment analysis on text
Let's first see how we can perform sentiment analysis using the text analytics service from *Azure*. Before starting, you will need two things:
* The subscription key for the service
* The endpoint for your service

You can find these details on your *Azure Portal* by viewing the image below.

**Explanation:**
* Once you are on your text analytics resource dashboard, click on the "*Keys and Endpoint*" button from the left navigation bar. There you can see the two keys (you can use any one of them to call your endpoint) and your endpoint.

Once you have those details, we can write some code to call the endpoint and send our text data to get the sentiment of the text.

Learn to use the text analytics service from Azure to identify the sentiment and extract key phrases from the text using Python.

Overview of the Course

Building Blocks of an API in Python

Learn the Basics of FastAPI

Optical Character Recognition API Using Tesseract - Project 1

Cloud and Microsoft Azure Services

Text Analytics API Using Azure Cognitive Services - Project 2

Deploy the REST API on Microsoft Azure

Pop Quiz

Appendix

Perform Sentiment and Key Phrase Extraction on Text

Perform sentiment analysis on text