Felix Wonfg
成为会员时间：2020
白银联赛
3100 积分
DevOps 基础知识
Earned May 26, 2020 EDT
面向机器学习的 BigQuery
Earned May 22, 2020 EDT
Google Cloud Solutions I: Scaling Your Infrastructure
Earned Apr 30, 2020 EDT
Machine Learning APIs
Earned Apr 29, 2020 EDT
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned Apr 29, 2020 EDT
Scientific Data Processing
Earned Apr 29, 2020 EDT
DEPRECATED Application Development - Python
Earned Apr 29, 2020 EDT
Cloud Architecture - Design, Implement, and Manage
Earned Apr 28, 2020 EDT
DEPRECATED Applied Data: Blockchain
Earned Apr 26, 2020 EDT
Cloud Healthcare API
Earned Apr 26, 2020 EDT
机器学习简介：语言处理
Earned Apr 24, 2020 EDT
[Deprecated] Advanced ML: ML Infrastructure
Earned Apr 24, 2020 EDT
Intro to ML: Image Processing
Earned Apr 24, 2020 EDT
[Deprecated] Data Science on Google Cloud: Machine Learning
Earned Apr 23, 2020 EDT
DEPRECATED Cloud Architecture
Earned Apr 23, 2020 EDT
VM Migration
Earned Apr 20, 2020 EDT
Google Cloud 基础知识
Earned Apr 19, 2020 EDT
Data Science on Google Cloud
Earned Apr 15, 2020 EDT
Intermediate ML: TensorFlow on Google Cloud
Earned Apr 13, 2020 EDT
[DEPRECATED] Data Engineering
Earned Apr 10, 2020 EDT
基准：数据、机器学习和 AI
Earned Apr 8, 2020 EDT
DEPRECATED BigQuery Basics for Data Analysts
Earned Apr 6, 2020 EDT
通过 DevOps 获得 竞争优势。DevOps 是一种组织和文化运动，旨在加快软件交付速度， 提高服务可靠性，并在软件利益相关方之间 建立共享所有权。在本课程中，您将学习如何使用 Google Cloud 提高 软件交付能力，确保较高的速度、稳定性、可用性和安全性。 DevOps Research and Assessment 已加入 Google Cloud。您的团队表现如何？参加 这个包含 5 道选择题的测验，来一探究竟吧！
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想要仅使用 SQL 就能在几分钟内构建机器学习模型，而不是花费数小时？BigQuery 借助机器学习，数据分析师能够使用现有的 SQL 工具和技能创建、训练、评估机器学习模型，并使用这些模型进行预测， 从而实现机器学习的普及。在 本系列实验中，您将尝试不同的模型类型，并了解 如何构建出色的模型。
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In this course you will learn how you to harness serious Google Cloud power and infrastructure. The hands-on labs will give you use cases and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your Google Cloud projects to the next level.
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It's no secret that machine learning is one of the fastest growing fields in tech, and Google Cloud has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API. Looking for a hands-on challenge lab to demonstrate your skills and validate your knowledge? Enroll in and finish the additional challenge lab at the end of this quest to receive an exclusive Google Cloud digital badge.
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In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs.
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Big data, machine learning, and scientific data? It sounds like the perfect match. In this course, you will get hands-on practice with GCP services like BigQuery, Managed Service for Apache Spark, and Tensorflow by applying them to use cases that employ real-life, scientific data sets. By getting experience with tasks like earthquake data analysis and satellite image aggregation, Scientific Data Processing will expand your skill set in big data and machine learning so you can start tackling your own problems across a spectrum of scientific disciplines.
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In this advanced-level quest, you will learn the ins and outs of developing GCP applications in Python. The first labs will walk you through the basics of environment setup and application data storage with Cloud Datastore. Once you have a handle on the fundamentals, you will get hands-on practice deploying Python applications on Kubernetes and App Engine (the latter is the same framework that powers Snapchat!) With specialized bonus labs that teach user authentication and backend service development, this quest will give you practical experience so you can start developing robust Python applications straight away.
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This quest of "Challenge Labs" gives the student preparing for the Google Cloud Certified Professional Cloud Architect certification hands-on practice with common business/technology solutions using Google Cloud architectures. Challenge Labs do not provide the "cookbook" steps, but require solutions to be built with minimal guidance, across many Google Cloud technologies. All labs have activity tracking, and in order to earn this badge you must score 100% in each lab. This quest is not easy and will put your Google Cloud technology skills to the test! Be aware that while practice with these labs will increase your knowledge and abilities, additional study, experience, and background in cloud architecture is recommended to prepare for this certification. Complete this quest to receive an exclusive Google Cloud digital badge.
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Blockchain and related technologies, such as distributed ledger and distributed apps, are becoming new value drivers and solution priorities in many industries. In this course you will gain hands-on experience with distributed ledger and the exploration of blockchain datasets in Google Cloud. It brings the research and solution work of Google's Allen Day into self-paced labs for you to run and learn directly. Since this course uses advanced SQL in BigQuery, a SQL-in-BigQuery refresher lab is at the start.
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Cloud Healthcare API bridges the gap between care systems and applications built on Google Cloud. By supporting standards-based data formats and protocols of existing healthcare technologies, Cloud Healthcare API connects your data to advanced Google Cloud capabilities, including streaming data processing with Cloud Dataflow, scalable analytics with BigQuery, and machine learning with Cloud Machine Learning Engine. In this Quest you will use the Cloud Healthcare API to ingest and process data in the industry standard FHIR, HL7v2 and DICOM formats, train a TensorFlow model for prediction with FHIR data, and also gain practice with de-identification of datasets.
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众所周知，机器学习是发展最快的技术领域之一， Google Cloud Platform 在推动其发展方面发挥了重要作用。 GCP 提供了一系列 API，几乎可以满足任何机器学习作业的需求。在 本入门课程中，您将了解机器学习在语言处理方面的运用， 通过实操实验学习 如何从文本中提取实体，执行情感和语法分析，以及 使用 Speech-to-Text API 进行转写。
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Machine Learning is one of the most innovative fields in technology, and the Google Cloud Platform has been instrumental in furthering its development. With a host of APIs, Google Cloud has a tool for just about any machine learning job. In this advanced-level course, you will get hands-on practice with machine learning at scale and how to employ the advanced ML infrastructure available on Google Cloud.
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Using large scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. The Google Cloud Platform provides world class speed and accuracy via systems that can utilized by simply calling APIs. With these and a host of other APIs, GCP has a tool for just about any machine learning job. In this introductory quest, you will get hands-on practice with machine learning as it applies to image processing by taking labs that will enable you to label images, detect faces and landmarks, as well as extract, analyze, and translate text from within images.
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This is the second of two courses of hands-on labs derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this second Quest, covering chapter 9 through the end of the book, you extend the skills practiced in the first Quest, and run full-fledged machine learning jobs with state-of-the-art tools and real-world data sets, all using Google Cloud tools and services.
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This fundamental-level quest is unique amongst the other quest 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 Cloud Architect Certification. From IAM, to networking, to Kubernetes engine deployment, this quest is composed of specific labs that will put your Google Cloud knowledge to the test. Be aware that while practice with these labs will increase your skills and abilities, we recommend that you also review the exam guide and other available preparation resources.
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Google Cloud’s four step structured Cloud Migration Path Methodology provides a defined and repeatable path for users to follow when migrating and modernizing Virtual Machines. In this quest, you will get hands-on practice with Google’s current solution set for VM assessment, planning, migration, and modernization. You will start by analyzing your lab environment and building assessment reports with CloudPhysics and StratoZone, then build a landing zone within Google Cloud leveraging Terraform’s infrastructure-as-code templates, next you will manually transform a two-tier application into a cloud-native workload running on Kubernetes, and finally, transform a VM workload into Kubernetes with Migrate for Anthos and migrate a VM between cloud environments.
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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。
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This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform, 2nd Edition by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud tools and services.
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TensorFlow is an open source software library for high performance numerical computation that's great for writing models that can train and run on platforms ranging from your laptop to a fleet of servers in the Cloud to an edge device. This quest takes you beyond the basics of using predefined models and teaches you how to build, train and deploy your own on Google Cloud.
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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.
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大数据、机器学习和人工智能是当今计算领域的热门话题， 但这些领域的专业性很强，因而很难找到 入门资料。幸运的是，Google Cloud 在这些领域提供了方便用户使用的服务， 通过本入门级课程，您可以 开始学习使用 BigQuery、Cloud Speech API 和 Video Intelligence 等工具。
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Want to scale your data analysis efforts without managing database hardware? Learn the best practices for querying and getting insights from your data warehouse with this interactive series of BigQuery labs. BigQuery is Google's fully managed, NoOps, low cost analytics database. With BigQuery you can query terabytes and terabytes of data without having any infrastructure to manage or needing a database administrator. BigQuery uses SQL and can take advantage of the pay-as-you-go model. BigQuery allows you to focus on analyzing data to find meaningful insights.
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