Discover Google Cloud training your way

With 980+ learning activities to choose from, Google Cloud has designed our comprehensive catalog with you in mind. The catalog consists of a variety of activity formats for you to pick from. Choose from bite-size individual labs or multi-module courses that consist of videos, documents, labs, and quizzes. Our labs give you temporary credentials to actual cloud resources, so you can learn Google Cloud using the real thing. Earn badges for what you complete, define, track, and measure your success with Google Cloud!

FILTER BY
Clear all
  • Badge
  • Format (1)
  • Language

389 results
  1. Course Featured

    Reliable Google Cloud Infrastructure: Design and Process

    This course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine courses and assumes hands-on experience with the technologies covered in eithe…

  2. Course Featured

    Machine Learning Operations (MLOps): Getting Started

    This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professiona…

  3. Course Featured

    Production Machine Learning Systems

    This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed t…

  4. Course Featured

    API Design and Fundamentals of Google Cloud's Apigee API Platform

    In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API life cycle, and how the Apigee API platform helps you manage all aspects of the life cycle. You learn about how APIs can be designed using API proxies, and how APIs are packaged as API prod…

  5. Course Featured

    Managing and Securing the Apigee Hybrid API Platform

    This course discusses how environments are managed in Apigee hybrid, and how runtime plane components are secured. You will also learn how to deploy and debug API proxies in Apigee hybrid, and about capacity planning and scaling.

  6. Course Featured

    Computer Vision Fundamentals with Google Cloud

    This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear mod…

  7. Course Featured

    Recommendation Systems on Google Cloud

    In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

  8. Course Featured

    Networking in Google Cloud: Fundamentals

    Networking in Google cloud is a 6 part course series. Welcome to the first course of our six part course series, Networking in Google Cloud: Fundamentals.  This course provides a comprehensive overview of core networking concepts, including networking fundamentals, virtual private clouds (VPCs), and the sharin…

  9. Course Featured

    Launching into Machine Learning

    The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a …

  10. Course Featured

    TensorFlow on Google Cloud

    This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.