Skip to main content

Introducing Azure app services


Creating an App Service

Inside the Azure portal, I want to create a new App Service. So I'm going to come over into App Service. I can see that I currently have no App Services in this subscription, so I will click the add button




create an app Service is to select a starting template that might include some pre-configured software. For example, if I scroll down through the list of available templates here, I can see that I can start with a WordPress site running on the Linux operating system and many other options available. So when working with platform as a service features, that we don't worry about the operating system. I can see there's Web App + SQL, which will create an App Service for me plus give me an Azure SQL instance, but I'm going to look for just the basic, most simple web application and I click that, I come to another blade that opens where I can read through a description of Web App and then click create. 

First of all, I have to give my App Service a name. Now,  an App Service name also forms part of a domain name that We can give to our users to reach my web application. In other words, the name that We enter here like Acmecore will be part of the name acmecore. azurewebsites. net, that anybody can use to reach my web application.


After filling the above information you will be redirected to below screen. Update information and click on create.



Azure will take few moments to create the application for you.



Once done you will get below screen with your newly created application information.





Labels:

Comments

Post a Comment

Popular posts from this blog

Prompt Engineering Fundamentals

  Introduction Generative AI is a transformative technology capable of producing text, images, audio, and code in response to user prompts. This capability is powered by Large Language Models (LLMs) like OpenAI's GPT series, which are trained to understand and generate natural language. Interacting with these models via prompts allows users to harness their potential without needing technical expertise. This chapter explores the essentials of prompt engineering, a field dedicated to optimizing prompt design for consistent and high-quality responses. Learning Goals By the end of this lesson, you will be able to: Explain what prompt engineering is and why it matters. Describe the components of a prompt and how they are used. Learn best practices and techniques for prompt engineering. Apply learned techniques to real examples using an OpenAI endpoint. Learning Sandbox Prompt engineering is more art than science, requiring practice and iterative refinement. This lesson includes a Jupyt...
Post a Comment
Read more

AWS Cloud Containers

Amazon Web Services (AWS) offers a variety of services for deploying and managing applications in the cloud. One of these services is called Amazon Elastic Container Service (ECS), which allows you to run and manage Docker containers on AWS. Here is a brief overview of how Amazon ECS works: You package your application into a Docker container image and push it to a registry, such as Amazon Elastic Container Registry (ECR) or Docker Hub. You create an Amazon ECS task definition, which is a blueprint for your containerized application. The task definition specifies things like the Docker image to use, the CPU and memory requirements, and the environment variables to pass to the container. You create an Amazon ECS cluster, which is a group of Amazon EC2 instances that are running the Amazon ECS container agent. The cluster is where your tasks are placed and run. You create an Amazon ECS service, which is a long-running task that is hosted on your cluster. The service ensures that a speci...
Post a Comment
Read more

Identified COVID-19 in X-ray images with deep learning.

  Project structure :       Our coronavirus (COVID-19) chest X-ray data is in the dataset/ directory where our two classes of data are separated into covid/ and normal/   I have created train_covid19.py file to train the model.   Three command line arguments (parameters) required to run this file :   --dataset: The path to our input dataset of chest X-ray images. --plot: An optional path to an output training history plot. By default the plot is named plot.png unless otherwise specified via the command line. --model: The optional path to our output COVID-19 model; by default it will be named covid19.model.     To load our data, we grab all paths to images in in the --dataset directory. Then, for each imagePath, we:   ·           Extract the class label (either covid or normal) from the path. ·           Load the image, and preprocess it by convertin...
Post a Comment
Read more