How Google Does Machine Learning
Earned Oct 31, 2021 EDT
Preparing for your Professional Data Engineer Journey
Earned Oct 25, 2021 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Oct 21, 2021 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Oct 20, 2021 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned Oct 19, 2021 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Oct 18, 2021 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Oct 14, 2021 EDT
API Development on Google Cloud's Apigee API Platform
Earned Oct 12, 2021 EDT
API Security on Google Cloud's Apigee API Platform
Earned Oct 12, 2021 EDT
API Design and Fundamentals of Google Cloud's Apigee API Platform
Earned Oct 12, 2021 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Oct 12, 2021 EDT
基准：基础架构
Earned Oct 11, 2021 EDT
Implement Load Balancing on Compute Engine
Earned Oct 8, 2021 EDT
Google Cloud Essentials
Earned Oct 8, 2021 EDT
This course explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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This course helps learners create a study plan for the PDE (Professional Data Engineer) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
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Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.
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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.
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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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完成中级技能徽章课程利用 BigQuery ML 处理预测模型的工程师数据， 展示自己在以下方面的技能：利用 Dataprep by Trifacta 构建 BigQuery 数据转换流水线； 利用 Cloud Storage、Dataflow 和 BigQuery 构建提取、转换和加载 (ETL) 工作流； 利用 BigQuery ML 构建机器学习模型； 以及利用 Cloud Composer 跨多个位置复制数据。 技能徽章是由 Google Cloud 颁发的专属数字徽章，旨在认可 您对 Google Cloud 产品与服务的熟练度；您需要在 交互式实操环境中参加考核，证明自己运用所学知识的能力后才能获得此徽章。完成技能徽章课程和 作为最终评估的实验室挑战赛，即可获得数字徽章， 在您的人际圈中炫出自己的技能。
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In this course, you learn how to create APIs that utilize multiple services and how you can use custom code on Apigee. You will also learn about fault handling, and how to share logic between proxies. You learn about traffic management and caching. You also create a developer portal, and publish your API to the portal. You learn about logging and analytics, as well as CI/CD and the different deployment models supported by Apigee.
Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform.This is the third and final course of the Developing APIs with Google Cloud's Apigee API Platform course series.
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In this course, you learn how to secure your APIs. You explore the security concerns you will encounter for your APIs. You learn about OAuth, the primary authorization method for REST APIs. You will learn about JSON Web Tokens (JWTs) and federated security. You also learn about securing against malicious requests, safely sending requests across a public network, and how to secure your data for users of Apigee.
Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform.
This is the second course of the Developing APIs with Google Cloud's Apigee API Platform series. After completing this course, enroll in the API Development on Google Cloud's Apigee API Platform course.
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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 products to be used by app developers.
Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, build, secure, deploy, and manage API solutions using Google Cloud's Apigee API Platform. This is the first course of the Developing APIs with Google Cloud's Apigee API Platform series. After completing this course, enroll in the API Security on Google Cloud's Apigee API Platform course.
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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.
How Google Does Machine Learning
Earned Oct 31, 2021 EDT
Preparing for your Professional Data Engineer Journey
Earned Oct 25, 2021 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Oct 21, 2021 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Oct 20, 2021 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned Oct 19, 2021 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Oct 18, 2021 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Oct 14, 2021 EDT
API Development on Google Cloud's Apigee API Platform
Earned Oct 12, 2021 EDT
API Security on Google Cloud's Apigee API Platform
Earned Oct 12, 2021 EDT
API Design and Fundamentals of Google Cloud's Apigee API Platform
Earned Oct 12, 2021 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Oct 12, 2021 EDT
基准：基础架构
Earned Oct 11, 2021 EDT
Implement Load Balancing on Compute Engine
Earned Oct 8, 2021 EDT
Google Cloud Essentials
Earned Oct 8, 2021 EDT