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

# Introduction to Azure Cognitive Services
*Azure* describes Cognitive Services in the following words:


> **Cognitive Services** brings AI within reach of every developer- without requiring machine-learning expertise. All it takes is an API call to embed the ability to see, hear, speak, search, understand and accelerate decision-making into your apps.

*Azure Cognitive Services* are cloud services with **REST APIs** and client library **SDKs** available to help developers build cognitive intelligent applications without having direct artificial intelligence (Al) or data science skills or knowledge.

*Azure Cognitive Services* helps developers easily add cognitive/AI features into their applications.

The goal of *Azure Cognitive Services* is to help developers create applications that can see, hear, speak, understand, and even begin to reason without having extensive knowledge of AI.
# Advantages of Azure Cognitive Services
* **Easy adoption and minimal development effort:** *Azure Cognitive Services* do not require machine learning expertise to build Al solutions. You just need to call a simple *API*, and the pre-trained *AI* model will help you incorporate *AI* capabilities within your application.

* ***Cross-platform support:*** *Since the *Azure Cognitive Services* support devices and platforms across* *Windows, iOS, and Android*, developers can use any language (`C#`, `Python`, `Java`, `Node.js`, etc.) to easily integrate the technology in their applications.
* ***Rich user experiences:*** The decision services such as the *Personalizer* and *Content Moderator* helps create better user experiences and ensure that any offensive or unwanted content is processed appropriately to provide an engaging and intuitive user experience.
* ***Extract deeper insights quickly:*** The *Vision API's* computer vision, custom vision, face, form, and ink recognizer help extract deeper insights from any form of data.

# Pillars of Azure Cognitive Services
Azure Cognitive Services can be categorized into four main pillars. Let's discuss each in detail. 
## Vision
This type of service will help you identify and analyze content within images and videos. There are five different *APIs* under the *Vision* pillar:
* **Computer Vision:** It analyzes content in images and video.

* **Custom Vision:** This will help you customize image recognition to fit your business needs.
* **Face:** This helps detect and identify people and emotions in the images.
* **Form Recogniser:** It can extract text, key-value pairs, and tables from documents.
* **Video Indexer:** This analyzes the visual and audio channels of a video and indexes its content.
## Speech
This type of service will help you analyze and perform *AI* tasks related to speech. There are four different *APIs* under the *Speech* pillar:
* **Speech to Text:** This *API* can transcribe the audible speech into readable, searchable text.
* **Text to Speech:** It can convert text to lifelike speech for more natural interfaces.
* **Speech Translation:** This can integrate real-time speech translation into your apps.
* **Speaker Recognition:** This will help you identify and verify the people speaking based on audio. (This API is under *PREVIEW* while writing this course.)
## Language
This type of service will help you extract meaning from unstructured text. There are five different *APIs* under the *Language* pillar:
* **Immersive Reader:** This helps readers of all abilities comprehend text using audio and visual cues.
* **Language Understanding:** This will help build natural language understanding into apps, bots, and IoT devices.
* **QnA Maker:** It creates a conversational question and an answer layer over your data.
* **Text Analytics:** It can detect sentiment, key phrases, and named entities. (*We will be using this service in our project.*)
* **Translator:** This can detect and translate more than 90 supported languages.

## Decision
This type of service will help you make smarter decisions faster. There are four different *APIs* under the *Decision* pillar:
* **Anomaly Detector:** This can identify potential problems early on.
* **Content Moderator:** It detects potentially offensive or unwanted content.
* **Metrics Advisor:** It monitors metrics and diagnoses issues.(This *API* is under *PREVIEW* while writing this course.)
* **Personaliser:** This helps create rich, personalized experiences for every user.

Learn the AI-related services offered by 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 Cognitive Services

Introduction to Azure Cognitive Services