In this introductory-level quest, you will get hands-on practice with the Google Cloud Platform’s fundamental tools and services. GCP Essentials is the recommended first Quest for the Google Cloud learner—you will come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first GCP project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, GCP Essentials is a prime introduction to the platform’s basic features. 1-minute videos walk you through key concepts for each lab.
If you are a novice cloud developer looking for hands-on practice beyond GCP Essentials, this quest is for you. You will get practical experience through labs that dive into Cloud Storage and other key application services like Stackdriver and Cloud Functions. By taking this quest, you will develop valuable skills that are applicable to any GCP initiative. 1-minute videos walk you through key concepts for these labs.
In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL and end with building a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
Want to learn the core SQL and visualization skills of a Data Analyst? Interested on how to write queries that scale to petabyte-size datasets? Take the BigQuery for Analyst Quest and learn how to query, ingest, optimize, visualize, and even build machine learning models in SQL inside of BigQuery.
This advanced-level quest is unique amongst the other Qwiklabs offerings. The labs have been curated to give IT professionals 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 skills 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.
Google Cloud Application Programming Interfaces are the mechanism to interact with Google Cloud Services programmatically. This quest will give you hands-on practice with a variety of GCP APIs, which you will learn through working with Google’s APIs Explorer, a tool that allows you to browse APIs and run their methods interactively. By learning how to transfer data between Cloud Storage buckets, deploy Compute Engine instances, configure Dataproc clusters and much more, Exploring APIs will show you how powerful APIs are and why they are used almost exclusively by proficient GCP users. Enroll in this quest today.
C# has powered Windows .NET application development for nearly two decades and Google Cloud is committed to supporting developers getting their .NET workloads up and running on the GCP platform. In this quest, you will learn how to run C# apps in GCP, and specifically how to take your apps to the next level by interfacing them with the big data and machine learning APIs that are accessible now from C#. By enrolling in Developing Data and Machine Learning Apps with C# you will see firsthand how seamlessly GCP integrates with .NET workloads and what the possibilities are for leveraging big data and ML services in your own C# projects.
In this Quest, the experienced user of Google Cloud will learn how to describe and launch cloud resources with Terraform, an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. In these nine hands-on labs, you will work with example templates and understand how to launch a range of configurations, from simple servers, through full load-balanced applications.
In this advanced-level quest, you will learn how you to harness serious GCP power and infrastructure. The hands-on labs will give you use cases, and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your GCP projects to the next level.
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.