Temukan pelatihan Google Cloud sesuai keinginan Anda.
Dengan lebih dari 980 aktivitas pembelajaran yang dapat dipilih, Google Cloud telah merancang katalog yang komprehensif dengan mempertimbangkan kebutuhan Anda. Katalog ini terdiri dari berbagai format aktivitas yang dapat Anda pilih. Anda dapat memilih dari lab individual berdurasi singkat atau kursus multi-modul yang terdiri dari video, dokumen, lab, dan kuis. Lab kami memberi Anda kredensial sementara ke resource cloud nyata agar Anda dapat mempelajari Google Cloud secara langsung di platform aslinya. Dapatkan badge untuk aktivitas yang berhasil Anda selesaikan, serta tentukan target, lacak progres, dan ukur keberhasilan Anda di Google Cloud.
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Kursus Unggulan 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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Kursus Unggulan 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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Kursus Unggulan 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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Kursus Unggulan 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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Kursus Unggulan 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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Kursus Unggulan 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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Kursus Unggulan 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.
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Kursus Unggulan 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…
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Kursus Unggulan 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 …
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Kursus Unggulan 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.