This is a self-paced lab that takes place in the Google Cloud console. In this lab, you create several VPC networks and VM instances and test connectivity across networks.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will review the case study application, an online Quiz. You will store application data for the Quiz application in Cloud Datastore.

In this guided project, you will learn about working with Google's BigQuery which is allows easily work with and query massive datasets without worrying about time wasting or having the right infrastructure to analyze that data quickly. You will learn how to use big query to collect your data, query it with SQL and even do quick visualizations on it.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to leverage Google Cloud Network Tags to route traffic to specific internal TCP/UDP load balancers that frontend separate VM-Series firewalls.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you use API products to package your APIs, and create a developer portal so application developers can try your APIs and sign up to use them.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll deploy the Autoscaler tool for Cloud Spanner, a companion tool to Cloud Spanner, in the per-project configuration where the autoscaler tools are located in the same project as the Cloud Spanner instance being autoscaled.

This course is the second of a series that aims to prepare you for a role working in data analytics. In this course, you will be introduced to many of the core concepts of cloud computing. You will learn about the primary deployment models. We’ll go through the common cloud computing service models. The hands on material offers the opportunity to review and configure a cloud account. This course covers a wide variety of topics that are critical for understanding cloud computing and are designed to give you an introduction and overview as you begin to build relevant knowledge and skills.

This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.

In this two hours project, you understand how Amazon Rekognition works and will learn how to use the AWS SDK to Analyze Faces, detect objects and labels in image scenes, moderate images, identify celebrities and recognize and compare faces using Artificial Intelligence. Amazon Rekognition is one of the most used Artificial Intelligence services in AWS and popular to analyze images with huge confidence and low costs. Once you're done with this project, you will be able to use Amazon Rekognition to analyze your own images in just a few steps.

This is a self-paced lab that takes place in the Google Cloud console. The Cloud Natural Language API lets you extract entities and perform sentiment and syntactic analysis on text. Watch these short videos Gain Valuable Insights from Text with Cloud Natural Language and Cloud Natural Language: Qwik Start - Qwiklabs Preview.