Become a Google Cloud Platform expert with hands-on training.
We give you temporary credentials to Google Cloud Platform, so you can learn the cloud using the real thing – no simulations. From 30-minute individual labs to multi-day courses, from introductory level to expert, instructor-led or self-paced, with topics like machine learning, security, infrastructure, app dev, and more, we've got you covered.
Learn Google Cloud Platform’s fundamental tools and services. Come in with little or no cloud knowledge and come out with practical experience. From writing Cloud Shell commands and deploying a VM, to running applications on Kubernetes Engine with load balancing.
This advanced-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give developers hands-on practice with topics and services that appear in the Google Cloud Certified Associate Cloud Engineer Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your GCP architecture knowledge to the test. Be aware that while practice with these labs will increase your knowledge and abilities, you will need other preparation too. The exam is quite challenging and external studying, experience, and/or background in cloud architecture is recommended.
Kubernetes in the Google Cloud
Kubernetes is the most popular container orchestration system and it was designed specifically with Google Cloud Platform integration in mind. In this advanced-level quest, you will get hands-on practice configuring Docker images and containers, deploying fully-fledged Kubernetes Engine applications, and integrating Slackbot and MongoDB databases with Kubernetes. This quest will teach you the practical skills needed for integrating container orchestration into your own workflow.
Baseline: Data, ML, AI
Big data, machine learning, and artificial intelligence are today’s hot computing topics, but these fields are quite specialized and introductory material is hard to come by. Fortunately, GCP provides user-friendly services in these areas and Qwiklabs has you covered with this introductory-level quest, so you can take your first steps with tools like Big Query, Cloud Speech API, and Cloud ML Engine. Want extra help? 1-minute videos walk you through key concepts for each lab.
This advanced-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give developers hands-on practice with topics and services that appear in the Google Cloud Certified Professional Data Engineer Certification. From Big Query, to Dataproc, to Tensorflow, this quest is composed of specific labs that will put your GCP data engineering knowledge to the test. Be aware that while practice with these labs will increase your knowledge and abilities, you will need other preparation too. The exam is quite challenging and external studying, experience, and/or background in cloud data engineering is recommended.
If you are a novice cloud developer looking for hands-on practice with GCP’s core infrastructure services, do yourself a favor and enroll in this quest. As a student, you will get practical experience by taking labs that dive into Cloud Storage, computing engines like Kubernetes, and key application services like Stackdriver and Deployment Manager. By taking this quest, you will develop invaluable skills that apply to any GCP project.
Machine Learning APIs
It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering it’s development. With a host of APIs, GCP has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning APIs by taking labs like Implementing an AI Chatbot with Dialogflow and Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API.
Become a Cloud Expert
Infrastructure & DevOps
Implement, deploy, migrate and maintain applications in the cloud.
Websites & App Dev
For software engineers who develop applications in the cloud.
Design, build, analyze, and optimize big data solutions.
Write distributed machine learning models that scale.
Security, Backup & Recovery
Stay compliant and protect information, data applications and infrastructure.