On-demand activities
Google Cloud 根據您的需求規劃了全方位的課程內容，內含超過 980 項學習活動，並涵蓋多種活動型態，您可自由選擇。您可以選擇簡短的個別研究室，或是包含影片、文件、研究室和測驗的多單元課程。在研究室中，您可以透過臨時憑證實際使用雲端資源，直接累積 Google Cloud 實作經驗。完成課程可獲得徽章，讓您輕鬆掌握、追蹤及評估自己的 Google Cloud 學習成果！
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课程 精选 Feature Engineering
This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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课程 精选 Enterprise Database Migration
This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to Google Cloud while taking advantage …
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课程 精选 Networking in Google Cloud: Routing and Addressing
Welcome to the second course in the networking and Google Cloud series routing and addressing. In this course, we'll cover the central routing and addressing concepts that are relevant to Google Cloud's networking capabilities. Module one will lay the foundation by exploring network routing and addressing in Googl…
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课程 精选 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…
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课程 精选 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…
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课程 精选 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…
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课程 精选 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…
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课程 精选 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.
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课程 精选 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…
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课程 精选 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.