Google Cloud is now offering 30 days no-cost access to Google Cloud Skills Boost, the definitive destination for skills development, to complete role-based training.
Choose from the following eight learning paths, which include interactive labs and opportunities to earn skill badges to demonstrate your cloud knowledge: Getting Started with Google Cloud, Cloud Architect, Cloud Engineer, Data Analyst, Data Engineer, DevOps Engineer, Machine Learning Engineer and Cloud Developer learning path.
Read below to find out more about each learning path.
Getting Started with Google Cloud
In this path, you’ll learn about Google Cloud fundamentals such as core infrastructure, big data and machine learning (ML). You’ll also find out how to write gcloud commands, use Cloud Shell, deploy virtual machines, and run containerized applications on Google Kubernetes Engine (GKE).
If you’re looking to learn how to design, develop, and manage cloud solutions, this is the path for you. You’ll learn how to perform infrastructure tasks like using Cloud Monitoring, Cloud Identity and Access Management (Cloud IAM), and more. The path will end with how to architect with Google Compute Engine and GKE.
For a guided walkthrough of how to get started with Cloud IAM and Monitoring, register here to join me on February 10. You’ll also have a chance to get your questions answered live by Google Cloud experts via chat.
To learn how to plan, configure, set up, and deploy cloud solutions, take this learning path. You’ll learn how to get started with Google Compute Engine, Terraform in a cloud environment, GKE, and more.
This learning path will teach you how to gather and analyze data to identify trends and develop valuable insights to help solve problems. You’ll be introduced to BigQuery, Looker, LookML, BigQuery ML, and Data Catalog.
Interested in designing and building systems that collect the data used for business decisions? Select this path. You’ll learn how to modernize data lakes and data warehouses with Google Cloud. Afterwards, you will also discover how to use Dataflow for serverless data processing and more.
A DevOps Engineer is responsible for defining and implementing best practices for efficient and reliable software delivery and infrastructure management. This learning path will show you how to build an SRE culture, use Google Cloud Operations Suite for DevOps, and more.
Machine Learning Engineer
Choose this path for courses and labs on how to design, build, productionize, optimize, operate, and maintain ML systems. You’ll discover how to use TensorFlow, MLOps tools, VertexAI, and more.
A Cloud Developer designs, builds, analyzes, and maintains cloud-native applications. This path will teach you how to use Cloud Run and Firebase for serverless app development. You’ll also learn how to deploy to Kubernetes in Google Cloud.
By: Christine De Sario (Customer Engineer, Google Cloud)
Source: Google Cloud Blog