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Boost Productivity with Gemini in BigQuery
- 基础技能水平
- 4.9+
- 12 条评价
Use Gemini AI to boost your productivity in BigQuery. Explore data, accelerate code development, and discover visualization workflows.
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观看专家讲师的短视频,然后在浏览器中通过互动练习实践所学内容。
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Use Gemini AI to boost your productivity in BigQuery. Explore data, accelerate code development, and discover visualization workflows.
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In this course, you’ll explore the essentials of cybersecurity, including the security lifecycle, digital transformation, and key cloud computing concepts.
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Develop data pipelines with Apache Beam and Dataflow. Cover transforms, windowing, I/O connectors, schemas, state APIs, Beam SQL, and notebooks.
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Secure and monitor GKE production environments. Learn access control, logging, monitoring, CI/CD pipelines, and managed storage integration on Google Cloud.
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Youll learn about the different components inside a hypercomputer, like GPUs, TPUs, and CPUs, and discover how to pick the right one for your needs.
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Design and deploy high-performance AI/ML solutions using Google Clouds AI Hypercomputer, GPUs, TPUs, Compute, and Google Kubernetes Engine.
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Deploy and manage Kubernetes workloads on GKE. Cover networking, deployments, jobs, persistent storage, and data management in production environments.
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Work with Gemini AI models in BigQuery for sentiment analysis. Analyze customer reviews using SQL and Python notebooks with Gemini.
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This course equips security and data protection leaders with strategies to securely manage AI within their organizations.
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Learn MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
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Operate Dataflow pipelines in production. Learn monitoring, logging, troubleshooting, performance tuning, CI/CD, reliability, and templates.
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Learn how to implement the various flavors of ML: static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.
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Learn best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
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Well explore how CPUs, GPUs, and TPUs make AI tasks super fast, what makes each one unique, and how AI software gets the most out of them.
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Uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and explore how Vertex AI empowers AI teams.
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In this course, you’ll focus on developing capabilities in logging, security, and alert monitoring, along with techniques for mitigating attacks.
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Gemini Enteprise brings together AI agents, enterprise search, NotebookLM, and intelligent data access to solve organizational challenges.
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This course covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices.
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This course reviews the essential security features of Model Armor and equips you to work with the service.
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Gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks.
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This course is designed for developers, data scientists, and ML engineers interested in quickly deploying AI inference services on Cloud Run.
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Journey through the storage solutions available on Google Cloud, specifically tailored for AI and high-performance computing (HPC) workloads.
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You learn best practices for cloud applications, and how to select compute and data options to match your application use cases.
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This course explores identity management and access control within a cloud environment, covering authentication, authorization, auditing, and more.
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The course introduces the benefits of Gemini Code Assist and compares the features of the different Gemini Code Assist editions.
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With help from Gemini, you learn how to develop and build a web application, fix errors in the application, develop tests, and query data.
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It explores practical methods and tools to implement AI privacy and safety recommended practices.
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It discusses the importance of AI transparency for developers and engineers.
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Learn about gemini CLI installation and configuration, and introduces use cases and security best practices
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数据科学是一个专注于从数据中获取信息的专业领域。数据科学家使用编程技能、科学方法、算法等来分析数据,形成可操作的洞察。
您需要学习 Python 或 R 等编程语言,掌握数学和统计学原理。数据分析方法和数据科学工具的知识也是必不可少的。学习数据科学有很多方法。除了正式的教育途径,如学位或大学学习,还有很多其他资源可以帮助您按自己的节奏学习。除了在线课程和教程,还有书籍、视频等。
除了数学和统计学知识,数据科学家还需要 Python、R 和 SQL 等语言的编程技能。此外,数据科学需要处理大型数据集的能力、数据可视化、数据整理和数据库管理知识。机器学习和深度学习技能也很有用。
在专业领域,几乎每个行业都可以在某种程度上使用数据科学。医疗机构使用数据科学来检测和治疗疾病,金融公司用它来检测和预防欺诈。各种行业都将数据科学用于营销,如构建推荐系统和分析客户流失。
是的,数据科学是美国和全球增长最快的行业之一。它也是薪酬最高的职业之一。根据 Payscale 的数据,在美国,有经验的数据科学家平均收入为 97,609 美元,满意度评分为五星中的四星。
这里有几个需要考虑的因素。首先,数据科学学位的竞争可能很激烈,通常需要持续的高分。同样,数据科学所需的许多技能需要大量的学习和耐心。掌握所有必要的基础知识可能需要几个月的时间,还需要大量的实践经验才能获得入门级职位。
是的,您需要一些 Python、R、SQL、Java 和 C/C++ 等语言的编程经验。不过,由于语法相对简单,Python 编程语言通常是新手的首选。
对于没有编程经验和/或数学背景的人来说,通常需要 7 到 12 个月的密集学习才能达到入门级数据科学家的水平。但是,重要的是要记住,仅仅学习数据科学的理论基础可能不会让您成为真正的数据科学家。
掌握数据科学基础后,您可以专攻各种领域,包括机器学习、人工智能、大数据分析、商业分析和智能、数据挖掘等。
随时随地通过我们的移动课程和每日 5 分钟编程挑战提升技能。