Do you receive an extraordinary amount of job application emails every day? Moreover, do you have the time to screen and read each job applicant’s email? How can you efficiently filter all candidate emails and forward qualified applicants to hiring committees? In this beginner-level guided project "Process Forms with AI Model in Power Automate", we will use a pre-created job application form and create a model that will extract data from the form. We will then train the model with a couple of patterns, and create a flow that will use the model when candidate emails arrive. Consequently, when we receive a job application by email, the application will go through the flow and, if the candidate meets desired requirements, forward the email to the supervisor. The requirement for this project is having a Microsoft developer program account, but don’t worry, you will be given instructions on how to get it right here. If you are ready to make your and your colleagues’ lives easier by starting to automate manual, time-consuming processes, then this project is for you! Let's get started!

What's the quickest and most efficient way of keeping track of articles in stock? A company supply department usually counts articles manually - how many specific sodas, cookie packages, or any other articles they have. It's a time-consuming and error-prone process, but thankfully, it can be automated with AI Builder and Power Apps. In this beginner-level guided project, you will build and train an AI Builder model for counting objects in images and create a Power Apps application which will use that AI model to show you the number of articles found in images. Of course, the Power App can be shared with colleagues so they can use it too. All we need to do is take our phone, open the application, upload or take a photo of articles and let Power Apps and AI Builder do the rest. Is that a great improvement of a business process or what? The best thing about AI Builder and Power Apps is that anyone can learn to use them regardless of their educational background. We don’t need to be familiar with any programming language; basic knowledge of Excel-like formulas is enough for completing this project.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab, you will learn to write and use Vault Policies.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will connect Cloud Functions and Cloud Run services with Workflows and connect two public Cloud Functions services, one private Cloud Run service, and an external public HTTP API into a workflow.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will discover and use the basic functionality of Cloud Healthcare API using HL7v2 messages.

Have you ever wondered what’s in the small print of cloud contracts? Or who owns the information you upload to the cloud? And who can access and control it? If so, then this course is for you! You’ll learn to identify the legal risks involved in cloud computing transactions - and to suggest possible solutions. To understand how cloud computing works, we’ll first describe different kinds of cloud services and explain how they differ from in-house IT in terms of control, access, and cybersecurity. To understand how the law applies to cloud computing transactions, we’ll focus on the contracts between cloud providers and their customers. You’ll learn to identify and interpret key contract terms and to assess whether customers might negotiate better terms with providers. Finally, we’ll explain how English law protects trade secrets and copyright works stored in the cloud. If you’re a lawyer, this course will be your guide to cloud infrastructure and supply chains. If you have a technical or commercial background, this course will introduce you to key legal issues in contracts and intellectual property. In short, if you’re interested in how the law applies to cloud computing, this course is for you.

In this 1-hour long project-based course, you will learn how to (create Azure SQL Server database from Azure portal and with Data Migration Assistant, recognize regressed queries using Query Store and determine top resource consuming queries, get query wait statistics and queries with high variation). Try to limit this to 400-500 characters. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a self-paced lab that takes place in the Google Cloud console. This lab deployings a Kubernetes Engine cluster, then a simple web application fronted by a load balancer is deployed to the cluster. The web app publishes messages provided by the user to a Cloud Pub/Sub topic. You will see the correlated telemetry data from HTTP requests to the app will be available in the Cloud Trace Console.

In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.

By the end of this project, you will be able to monitor and manage your network, services and processes using linux. you will be able to identify RedHat and write basic commands in the shell. Moreover, you will be able to list services and daemons. Finally, you will be able to manage RedHat enterprise linux networking. The goal of this project is to learn basics about linux in the first task you will be able to open the terminal and know what is the shell and some basic commands