Course four of the Anthos series prepares students to consider multiple approaches for modernizing applications and services within Anthos environments. Topics include optimizing workloads on serverless platforms and migrating workloads to Anthos. This course is a continuation of course three, Anthos on Bare Metal, and assumes direct experience with the topics covered in that course.

In this lab you will upload existing data (a CSV file) to a Firestore serverless database in the cloud.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will set up the Data Loss Prevention API and and use the API to inspect a string of data for sensitive information.

This is a self-paced lab that takes place in the Google Cloud console. Use Migrate for Compute Engine to migrate an EC2 instance from AWS to Compute Engine on Google Cloud and verify the migration

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab you will explore using Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.

This is a self-paced lab that takes place in the Google Cloud console. Use the Variant Transforms tool to transform and load VCF files from Cloud Storage into BigQuery.

Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals. This is the second course in a five-course program that prepares you to take the AI-900 certification exam. This course teaches you the core concepts and skills that are assessed in the AI fundamentals exam domains. This beginner course is suitable for IT personnel who are just beginning to work with Microsoft Azure and want to learn about Microsoft Azure offerings and get hands-on experience with the product. Microsoft Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Microsoft Azure Data Scientist Associate or Microsoft Azure AI Engineer Associate, but it is not a prerequisite for any of them. This course is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial. To be successful in this course, you need to have basic computer literacy and proficiency in the English language. You should be familiar with basic computing concepts and terminology, general technology concepts, including concepts of machine learning and artificial intelligence.

In this 2-hour long project based course, we will look at how to handle and divert website traffic to multiple servers using Routing Policies in AWS Route 53. We will look at how you can configure different types of Routing Policies. We will start off with Simple Routing Policy which can be used to divert traffic to multiple servers / IP’s randomly. Then we will look at Weight Routing Policy which allows you to split your traffic based on different weights assigned. We will then move on to Latency-based Routing which allows you to route your traffic based on the lowest network latency for your end user (fastest response time). Then we will learn to create an active/passive set up using Failover Routing Policy where you can have a primary website and a secondary Disaster Recovery site.. We will then look at Geolocation Routing Policy which will send your traffic to various servers based on the Geographic location of your users which can for example allow for custom sites based on user location. Finally, we will see Multi-Value Answer Policy which lets you configure Route53 to return multiple values along with health checks. 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 course aimed at students wishing to develop Java based Web Applications and Restful Micro Services using the very popular Spring MVC and Spring Boot frameworks with minimal configuration. The student will develop services through various Url templates, consume and respond with json or XML payloads and create custom HTTP headers. Requestors of these services will include Java and Angular JS clients to illustrate the reuse capabilities of services in a distributed architecture. Traditional web applications will also be covered that render web pages in a typical Model View Controller (MVC) architecture. This is a very hands on course with a series of labs to illustrate the key concepts.

This is a self-paced lab that takes place in the Google Cloud console. Internal Load Balancer offers you the possibility to load balance TCP/UDP traffic without exposing your VMs via a public IP to the Internet. In this lab we will create a public-facing web server to serve the result of a simple web application.