Jithesh Os
成为会员时间:2019
白银联赛
1600 积分
成为会员时间:2019
这是一节入门级微课程,旨在解释什么是生成式 AI、它的用途以及与传统机器学习方法的区别。该课程还介绍了可以帮助您开发自己的生成式 AI 应用的各种 Google 工具。
各种规模的组织都在利用云的强大功能和灵活性来改变运营方式。然而,有效管理和扩缩云资源可能是一项复杂的任务。本课程探讨了云中现代运营、可靠性和弹性方面的基本概念,以及 Google Cloud 如何帮助支持这些工作。 作为 Cloud Digital Leader 学习路线的一部分,本课程旨在帮助个人在自己的岗位上成长,并为企业谋求未来发展。
许多传统企业都在使用无法满足现代客户期望的旧式系统和应用。业务领导者往往需要在维护老旧的 IT 系统和投资新产品与服务之间做出选择。本课程探讨了这些挑战,并提供了利用云技术克服这些挑战的解决方案。 作为 Cloud Digital Leader 学习路线的一部分,本课程旨在帮助个人在工作中成长,并为企业打造美好未来。
云技术是一项强大的资产,与数据相结合后,它将成为创新和提升客户体验的催化剂。《使用 Google Cloud 探索数据转换》课程探讨了组织如何利用云技术让数据更易于访问、更具实用价值和商业价值。 作为“Cloud Digital Leader 学习路线”的一部分,本课程旨在帮助个人提升职业素养,并为企业谋求未来发展。
数字化转型是现代组织的必经之路,而扎实掌握云计算基础知识,是推动实质性创新的第一步。《利用 Google Cloud 实现数字化转型》课程将讲解核心技术与战略框架,助力组织实现运营现代化。本课程介绍关于云的基本概念、全球网络基础设施和责任共担模式,帮助领导者从容推进上云进程。 作为 Cloud Digital Leader 学习路线的一部分,本课程旨在帮助学员提升岗位能力,从而为企业打造美好未来。
This advanced-level quest is unique amongst the other catalog 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 Dataprep, to Cloud Composer, this quest is composed of specific labs that will put your Google Cloud 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. Looking for a hands on challenge lab to demonstrate your skills and validate your knowledge? On completing this quest, enroll in and finish the additional challenge lab at the end of the Engineer Data in the Google Cloud to receive an exclusive Google Cloud digital badge.
在本入门级课程中,您将了解 Google Cloud 的基础工具和服务。此课程提供了可选视频, 旨在帮助您深入了解和回顾实验中涉及的概念。Google Cloud 基础知识是推荐给 Google Cloud 学员的第一门课程 - 即使您几乎没有云相关知识,也能从中获得实践 经验,并将其直接运用于您的首个 Google Cloud 项目。从编写 Cloud Shell 命令和部署您的第一个虚拟机,到在 Kubernetes Engine 上运行应用 或者使用负载均衡,“Google Cloud 基础知识”都是您了解该平台 基本功能的首选入门级课程。
In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting. Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.