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

# The Text Analytics Cognitive API
The **Text Analytics API** is a cloud-based service that provides advanced natural language processing over raw text and includes the following four main functions: 
* Sentiment Analysis
* Key Phrase Extraction 
* Named Entity Recognition
* Language Detection

This service is a part of one of the cognitive services from Microsoft Azure. In this course, we are going to use this service to identify sentiments and extract key phrases from a list of texts. Let us quickly discuss these four use-cases and then we will start with our project.

## Sentiment analysis
With *sentiment analysis* we can find out what people think of our brand or topic by mining the text for clues about positive or negative sentiment.

The service provides sentiment labels (such as *negative*, *neutral*, and *positive*) based on the highest confidence score found by the service at a sentence and document level. This feature also returns confidence scores between `0` and `1` for each document & sentences within it for *positive*, *neutral*, and *negative* sentiment.

## Key phrase extraction
We can use key-phrase extraction to quickly identify the main concepts in the text. For example, in the text "*The food was delicious and there were wonderful staff*", the *Key Phrase Extraction* will return the following main talking points:  
* *food*
* *wonderful staff*

## Language detection
Language detection can detect the language an input text is written in and report a single language code for every document submitted on the request in a wide range of languages. The language code is associated with a confidence score.

## Named entity recognition
*Named Entity Recognition (NER)* can identify and categorize entities in your text as people, places, organizations, quantities, etc. Well-known entities are also recognized and linked to more information (*usually Wikipedia*) on the web.


# Create a Text Analytics Cognitive API resource

Now that we understand the use-cases of this service, let's go ahead and create the service in *Azure*.

Learn about the Text Analytics Cognitive API from Microsoft Azure.

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

Azure Text Analytics Cognitive API

The Text Analytics Cognitive API