Introduction to AI and Machine Learning on Google Cloud
Earned fév. 23, 2025 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned jan. 16, 2025 EST
Developing a Google SRE Culture
Earned déc. 27, 2024 EST
Google Cloud Fundamentals: Core Infrastructure
Earned déc. 26, 2024 EST
Preparing for Your Professional Cloud Security Engineer Journey
Earned déc. 26, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned déc. 22, 2024 EST
Introduction to Data Engineering on Google Cloud
Earned déc. 20, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned déc. 18, 2024 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned déc. 18, 2024 EST
Set Up a Google Cloud Network
Earned déc. 16, 2024 EST
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned déc. 8, 2024 EST
How Google Does Machine Learning
Earned oct. 2, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned sept. 18, 2024 EDT
Preparing for your Professional Cloud Architect Journey
Earned juin 19, 2024 EDT
This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
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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 either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to define and balance business and technical requirements to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective.
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In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.
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Google Cloud Fundamentals: Core Infrastructure introduces important concepts and terminology for working with Google Cloud. Through videos and hands-on labs, this course presents and compares many of Google Cloud's computing and storage services, along with important resource and policy management tools.
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This course helps learners prepare for the Professional Cloud Security Engineer (PCSE) Certification exam. Learners will be exposed to and engage with exam topics through a series of lectures, diagnostic questions, and knowledge checks. After completing this course, learners will have a personalized workbook that will guide them through the rest of their certification readiness journey.
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While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
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In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.
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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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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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Earn a skill badge by completing the Set Up a Google Cloud Network skill badge course, where you will learn how to perform basic networking tasks on Google Cloud Platform - create a custom network, add subnets firewall rules, then create VMs and test the latency when they communicate with each other.
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This course on Integrate Vertex AI Search and Conversation into Voice and Chat Apps is composed of a set of labs to give you a hands on experience to interacting with new Generative AI technologies. You will learn how to create end-to-end search and conversational experiences by following examples. These technologies complement predefined intent-based chat experiences created in Dialogflow with LLM-based, generative answers that can be based on your own data. Also, they allow you to porvide enterprise-grade search experiences for internal and external websites to search documents, structure data and public websites.
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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 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.
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This course helps learners create a study plan for the PCA (Professional Cloud Architect) 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.
Introduction to AI and Machine Learning on Google Cloud
Earned fév. 23, 2025 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned jan. 16, 2025 EST
Developing a Google SRE Culture
Earned déc. 27, 2024 EST
Google Cloud Fundamentals: Core Infrastructure
Earned déc. 26, 2024 EST
Preparing for Your Professional Cloud Security Engineer Journey
Earned déc. 26, 2024 EST
Build Data Lakes and Data Warehouses on Google Cloud
Earned déc. 22, 2024 EST
Introduction to Data Engineering on Google Cloud
Earned déc. 20, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned déc. 18, 2024 EST
DEPRECATED Google Cloud Solutions II: Data and Machine Learning
Earned déc. 18, 2024 EST
Set Up a Google Cloud Network
Earned déc. 16, 2024 EST
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned déc. 8, 2024 EST
How Google Does Machine Learning
Earned oct. 2, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned sept. 18, 2024 EDT
Preparing for your Professional Cloud Architect Journey
Earned juin 19, 2024 EDT