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, 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 learn how to train and deploy a Semantic Segmentation model using Amazon Sagemaker. Sagemaker provides a number of machine learning algorithms ready to be used for solving a number of tasks. We will use the semantic segmentation algorithm from Sagemaker to create, train and deploy a model that will be able to segment images of dogs and cats from the popular IIIT-Oxford Pets Dataset into 3 unique pixel values. That is, each pixel of an input image would be classified as either foreground (pet), background (not a pet), or unclassified (transition between foreground and background). Since this is a practical, project-based course, we will not dive in the theory behind deep learning based semantic segmentation, but will focus purely on training and deploying a model with Sagemaker. You will also need to have some experience with Amazon Web Services (AWS).

Use Microsoft Azure services to create a Kubernetes deployment of a simple voting application with a Redis backend.

Vast amounts of personal information are processed in the cloud. But who is legally responsible for such ‘personal data’ in cloud environments? What duties do cloud providers like Amazon, Microsoft, and Google have? And what rights can you, as an individual, exercise under data protection law? If you’d like to find out, then this course is for you! First, we’ll look at how the European Union’s ‘General Data Protection Regulation’ (‘GDPR’) regulates the processing of personal data in cloud services. You’ll learn to identify controllers and processors, describe their roles and responsibilities, and understand how cloud customers and providers can comply in practice. Second, we’ll look at international transfers of personal data. We’ll explain how the GDPR can apply to cloud providers and their customers anywhere in the world, as well as how restrictions on international transfers apply to cloud services. Third, we’ll look at how the Network and Information Security (‘NIS’) Directive regulates the cybersecurity of critical infrastructure. You’ll learn to identify cloud providers’ duties to notify security breaches and to keep their services secure, and how to apply those duties to concrete case studies. In short, this course covers how the GDPR and NIS Directive apply to cloud services and what cloud providers and their customers should do to comply.

Have you thought about creating a query that can be called several times to perform a routine task? Stored procedures offer this with a great advantage of efficiency. This project-based course, "Working with SQL Stored Procedures using MySQL Workbench" is intended for intermediate SQL users with some related experiences with SQL and who are willing to advance their knowledge and skills. In this 2-hour project-based course, you will learn how to create stored procedures for different tasks including stored procedures with one input parameter, multiple input parameters, and an output parameter(s). This course is structured in a systematic way and very practical, where you get an option to practice as you progress. This project-based course is an intermediate-level course in SQL. Therefore, to get the most out of this project, it is essential to understand using SQL. Specifically, you should be able to write SQL JOIN statements and work with aggregate functions. If you are not familiar with these concepts, it will be helpful to complete my previous project titled "Performing Data Aggregation using SQL Aggregate Functions" and “Mastering SQL Joins.” However, if you are comfortable with these SQL concepts, please join me on this wonderful ride! Let’s get our hands dirty!

This is a self-paced lab that takes place in the Google Cloud console. This lab provides a high level overview of the Google Cloud Adoption Framework and then deep dives into how to automate environment setup and deployments on GCP.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab, you turn your code into a replicated application running on Kubernetes. You create a Node.js app, create a Docker container from it, deploy it to Kubernetes Engine, and scale it up. Watch a short preview Hello Node Kubernetes, GCP Essentials.

This is a self-paced lab that takes place in the Google Cloud console. This hands-on lab shows you how to query public tables and load sample data into BigQuery using the Command Line Interface. Watch the short videos Get Meaningful Insights with Google BigQuery and BigQuery: Qwik Start - Qwiklabs Preview.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab you will import existing infrastructure, write Terraform configuration that matches that infrastructure, and manipulate state storage with Terraform.

In this course you will be introduced to two methods of writing SQL queries that retrieve data from two or more tables. Since one of the functions of a database is to store data in an organized format, many databases are made up of multiple tables. Often, the data output required from the database is made up of data from more than one table. For example, the data that populates a student transcript might come from the Student, Course, and Section tables. While the Student table may provide the student’s name, the name and number of the course might come from the Course table and the specific grade for that course may come from yet another table. While writing SQL queries in SQLiteStudio, you'll learn the SQL syntax required to join tables together as you develop an understanding of how the relationships among tables come into play. 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 1-hour long project-based course on Intermediate Docker: Containerize a full-stack NodeJS application in Docker and deploy to remote server, you will get to use Docker to stitch together a real full stack NodeJS application in a multi-container architecture. By the end of this course, you will have built a simple blog page, which is a working full-stack application using the MVC (model view controller) framework in NodeJS persisting to a MongoDB database across separate containers and living on one single Docker provided network. You will learn how to stitch all of these services together using Dockerfile and docker-compose files, and get it configured in a remote machine in the cloud, just like if you wanted to run the application from anywhere in the world! 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.