Incorporating machine learning into data pipelines increases the ability of businesses to extract insights from their data. This course covers several ways machine learning can be included in data pipelines on Google Cloud depending on the level of customization required. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions using Vertex AI. Learners will get hands-on experience building machine learning models on Google Cloud using QwikLabs.

Welcome to this hands-on project on using Microsoft’s Custom Vision service for automated machine learning or AutoML as it’s popularly known. In this project, you are going to use Microsoft’s drag and drop tool to train your computer to recognize images of dogs and cats. We are going to do all of this without writing a single line of code! To take this guided-project, you do not need a background in computer science, machine learning or coding. The only prerequisite for this project is that you have a Microsoft Azure account. If you don’t already have one, you will have to sign up for it. 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 self-paced lab that takes place in the Google Cloud console. In this lab, you will learn how to secure a Compute Engine instance with Identity-Aware Proxy (IAP).

This on-demand course equips students to build reliable and manageable multi-cluster Kubernetes infrastructures using Anthos GKE, whether deployed with Anthos on Google Cloud or with Anthos deployed on VMware. It is a continuation of Architecting with GKE and assumes hands-on experience with the technologies covered in that course.

This is a self-paced lab that takes place in the Google Cloud console. Architecting Hybrid Infrastructure with Anthos: Adopt service mesh authentication and authorization using Istio

This is a Google Cloud Self-Paced Lab. Learn how to add recovery information to your Workspace admin account and reset your admin password.

This is a self-paced lab that takes place in the Google Cloud console. Deploy and test a visual inspection AI cosmetic anomaly detection solution.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab, you will learn how to create dynamic secrets in Vault.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you explore a Bigtable instance and use the Bigtable CLI (cbt CLI) to query data in Bigtable. You also design a table schema and row key using best practices for Bigtable.

In this course, you will learn how to create and manage data pipelines in the cloud using Azure Data Factory. This course is part of a Specialization intended for Data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services. It is ideal for anyone interested in preparing for the DP-203: Data Engineering on Microsoft Azure exam (beta). This is the third course in a program of 10 courses to help prepare you to take the exam so that you can have expertise in designing and implementing data solutions that use Microsoft Azure data services. The Data Engineering on Microsoft Azure exam is an opportunity to prove knowledge expertise in integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. Each course teaches you the concepts and skills that are measured by the exam. By the end of this Specialization, you will be ready to take and sign-up for the Exam DP-203: Data Engineering on Microsoft Azure (beta).