This is a self-paced lab that takes place in the Google Cloud console. In this lab you explore various ways to load data into Cloud Spanner as well as perform a backup of your database.

The purpose of the course is to help students understand the legal implications related to the design and use of artificial intelligence systems, providing an overview of the risks and legal protections that can be envisaged and giving an overview of the legislation and legal principles currently applicable on the subject. In particular, the profiles of civil and criminal liability, protection in terms of intellectual property and the impacts of AI on the fundamental rights of the individual - including privacy and the right to non-discrimination – will be examined.

This course helps you structure your preparation for the Professional Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.

In this guided project, you will learn how to use Amazon Transcribe to generate your own audio transcripts quickly.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you use the What-if Tool to identify potential biases in a model trained on mortgage loan applications.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you use an Apigee X threat protection policy and Cloud Armor to protect your APIs.

In this Guided Project, you will familiarize yourself with the basics of Azure Serverless Functions and Azure API Management using NodeJS. You're going to create a Function App and two different Serverless Functions, triggered by HTTP calls. You're going to set them up to receive data using both query parameters and path parameters, thus understanding the role of route templates. Then you're going to set up an API Management instance in order to expose them as an unique, homogeneous API that uses path parameters by using inbound processing rules. Note: before taking this Guided Project, if you don't have an Azure subscription yet, please create an Azure Free Trial beforehand at https://portal.azure.com 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.

Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Cloud Bigtable for analysis. Learners will get hands-on experience building streaming data pipeline components on Google Cloud using QwikLabs.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you’ll be working with Wrangler directives which are used by the Wrangler plugin, the “Swiss Army Knife” of plugins in the Data Fusion platform, so that your transformations are encapsulated in one place and we can group transformation tasks into manageable blocks.

This is a self-paced lab that takes place in the Google Cloud console. Demonstrate how to build a REST API with Go and Cloud Run