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

# What we covered in this course
This is the last lesson in this course. Let's quickly summarize what we have learned in this course.
* We covered the basics of a **REST API**.
* We understood the following concepts: 
   * **Synchronous Web Apps:** These apps will start executing at the very top of a file and execute all the way down to the bottom of the file. These web apps run each line in the order until they get to the bottom of the file, which is when they stop.
   * **Asynchronous Web Apps:** These apps start at the top of the file and execute the code until the bottom is reached, but during that execution, this kind of web app will run into certain asynchronous functions or code that will split off and execute that asynchronous code separately from the rest of the code.
   * **Concurrency:** It means executing multiple tasks at the same time but not necessarily simultaneously.
   * **Parallelism:** It means executing multiple tasks at the same time simultaneously.

   We also implemented concurrency using **Async-Await** in **Python**.
* We discussed the **FastAPI** web framework for **Python**. We covered the implementation of different types of parameters in **FastAPI** such as *path parameters*, *query parameters*, *default parameters*, *optional parameters*, *request body*.
* We also covered the implementation of background processing and concurrent processing using **FastAPI**.
* We made our first project based on *Optical Character Recognition* (**API**). We learned how to process multiple images concurrently to save some execution time.
* We discussed the concepts of cloud technology and *Microsoft Azure*. We also covered the following *Azure* services:
   * *Azure App Services*
   * *Azure Cognitive Services*
   * *Azure Application Insights*
* Then, we created our second project based on *Azure Cognitive Services* and more specifically, on the *Azure Text Analytics Services* (*a part of Azure Cognitive Services*). 
* We learned how to deploy an **API** on Azure App Services.
* We also implemented logging using *Azure Application Insights*.
* Finally, we learned the process of *CI/CD* using GitHub actions integrated with *Azure App Services*.



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Testimonials#

We hope you enjoyed the course and learned many new concepts. Try to attempt the quiz next in the next lesson to test your understanding of the concepts.

We would really love to hear your feedback on this course. It will help us to improve the course and deliver rich content in the future. You can fill in the [testimonial here](https://forms.gle/NCSGhawgi9t2zY5p7).

We really appreciate your time.

This lesson offers a review of concepts that were covered in this course.

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

Summary

What we covered in this course