This is a self-paced lab that takes place in the Google Cloud console. In this lab, we will walk through deploying a sample application and enforcing the access restriction capabilities using Identity-Aware Proxy.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will build a time series model to forcast demand of multiple products using BigQuery ML. This lab is based on a blog post and featured in an episode of Cloud OnAir.

In this course brought to you by industry leader Check Point, they will cover the Maestro Orchestrator initial installation, creation and configuration of security group via the web user interface and SmartConsole features. This course provides a demonstration of the Maestro product. Course will prepare you for their exam, #156-412, at PearsonVUE.

In this course, we are going to focus on the following learning objectives: 1. Work with core Python programming tools 2. Become comfortable reading and writing Python scripts By the end of this course, you will have a solid grasp of scripting in Python. You will learn the Pythonic way of many of the core programming concepts. You will be able to read and understand Python scripts in your daily line of work

Please note: You will need an AWS account to complete this course. Your AWS account will be charged as per your usage. Please make sure that you are able to access Sagemaker within your AWS account. If your AWS account is new, you may need to ask AWS support for access to certain resources. You should be familiar with python programming, and AWS before starting this hands on project. We use a Sagemaker P type instance in this project for training the model, and if you don't have access to this instance type, please contact AWS support and request access. In this 2-hour long project-based course, you will how to train and deploy a Recommendation System using AWS Sagemaker. We will go through the detailed step by step process of training a recommendation system on the Amazon's Electronics dataset. We will be using a Notebook Instance to build our training model. You will learn how to use Apache's MXNET Deep Learning Model on the AWS Sagemaker platform. Since this is a practical, project-based course, we will not dive in the theory behind recommendation systems, but will focus purely on training and deploying a model with AWS Sagemaker. You will also need to have some experience with Amazon Web Services (AWS) and knowledge of how deep learning frameworks work. 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.

In this 90 minutes long project-based course, you will learn how to create a Microsoft Azure ML Studio account, a Kaggle account for competitions and use both of them to build a machine learning model which we will be using to make predictions. 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.

In this Guided Project you will create a MongoDB database and collection to store blog posts, and optimize it using indexes, while having an overview of some of the basic commands, in order to learn the basics of NoSQL document databases structure, the MongoDB shell and the usage of the powerful MongoDB Compass GUI to manage, inspect and optimize a MongoDB database. NoSQL is an alternative to traditional relational databases. NoSQL databases sacrifice some relational databases characteristics, such as a well-defined structure and strict relations between entities, in order to achieve better and easier scaling and replication, to handle large quantities of data, while being more generally flexible, cheaper and easier to manage. Instead of using tables, rows and columns, NoSQL document databases such as MongoDB use collections, documents and fields, represented with the well known JSON format. 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.

In this course, you will learn how Azure Functions enable the creation of event-driven, compute-on-demand systems that can be triggered by various external events. You will earn how to leverage functions to execute server-side logic and build serverless architectures. This course will help you prepare for the Microsoft Certified: Azure Developer Associate certification. This course is part of a Specialization intended for developers who want to demonstrate their expertise in all phases of cloud development from requirements, definition, and design; to development, deployment, and maintenance; to performance tuning and monitoring. It is ideal for anyone interested in preparing for the AZ-204: Developing Solutions for Microsoft Azure exam. This is the first course in a program of 8 courses to help prepare you to take the exam. Each course teaches you the concepts and skills that are measured by the exam. By the end of this Professional Certificate program, you will be ready to take and sign-up for the Exam AZ-204: Developing Solutions for Microsoft Azure.

By the end of this project, you will create a full stack web application using React on the front end and Express along with MongoDB and Node.js on the back end. Creating a full stack web application with React and Express allows the developer to use JavaScript throughout the stack. Express provides an API that simplifies the interaction with the Node.js server.

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