Generative AI for Business Leaders
Earned sept. 22, 2024 EDT
Introduction to Responsible AI
Earned sept. 22, 2024 EDT
Generative AI Fundamentals
Earned sept. 22, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned sept. 22, 2024 EDT
Serverless Data Processing with Dataflow: Operations
Earned sept. 20, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned juil. 17, 2024 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned juil. 16, 2024 EDT
Preparing for your Professional Cloud Architect Journey
Earned juil. 16, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned juin 28, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned mai 14, 2024 EDT
Preparing for your Professional Data Engineer Journey
Earned mai 12, 2024 EDT
Gemini for Data Scientists and Analysts
Earned mai 6, 2024 EDT
Essential Google Cloud Infrastructure: Core Services
Earned fév. 25, 2024 EST
Preparing for Your Associate Cloud Engineer Journey
Earned avr. 26, 2023 EDT
Essential Google Cloud Infrastructure: Foundation
Earned mars 1, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned fév. 8, 2023 EST
Gen AI: Navigate the Landscape is the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.
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Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google Cloud strategies to address the limitations of foundation models and the key challenges for responsible and secure AI development and deployment.
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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI course, where you will learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models. This skill badge course is for professional Data Scientists and Machine Learning Engineers. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this Skill Badge, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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This course introduces Vertex AI Studio, a tool to interact with generative AI models, prototype business ideas, and launch them into production. Through an immersive use case, engaging lessons, and a hands-on lab, you’ll explore the prompt-to-product lifecycle and learn how to leverage Vertex AI Studio for Gemini multimodal applications, prompt design, prompt engineering, and model tuning. The aim is to enable you to unlock the potential of gen AI in your projects with Vertex AI Studio.
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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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The course begins with a discussion about data: how to improve data quality and perform exploratory data analysis. We describe Vertex AI AutoML and how to build, train, and deploy an ML model without writing a single line of code. You will understand the benefits of Big Query ML. We then discuss how to optimize a machine learning (ML) model and how generalization and sampling can help assess the quality of ML models for custom training.
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.
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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 3 AI principles.
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Earn a skill badge by passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you.
In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.
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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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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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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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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.
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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.
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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 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 course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps analyze customer data and predict product sales. You also learn how to identify, categorize, and develop new customers using customer data in BigQuery. Using hands-on labs, you experience how Gemini improves data analysis and machine learning workflows.
Duet AI was renamed to Gemini, our next-generation model.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems and applications services. This course also covers deploying practical solutions including customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.
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This course helps you structure your preparation for the Associate Cloud Engineer exam. You will learn about the Google Cloud domains covered by the exam and how to create a study plan to improve your domain knowledge.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud with a focus on Compute Engine. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, virtual machines and applications services. You will learn how to use the Google Cloud through the console and Cloud Shell. You'll also learn about the role of a cloud architect, approaches to infrastructure design, and virtual networking configuration with Virtual Private Cloud (VPC), Projects, Networks, Subnetworks, IP addresses, Routes, and Firewall rules.
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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.
Gen AI: Navigate the Landscape
Earned mai 30, 2025 EDT
Gen AI: Unlock Foundational Concepts
Earned mai 30, 2025 EDT
Build and Deploy Machine Learning Solutions on Vertex AI
Earned nov. 26, 2024 EST
Building Batch Data Pipelines on Google Cloud
Earned nov. 3, 2024 EST
Introduction to Vertex AI Studio
Earned oct. 8, 2024 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned oct. 8, 2024 EDT
Launching into Machine Learning
Earned oct. 8, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned oct. 8, 2024 EDT
Generative AI for Business Leaders
Earned sept. 22, 2024 EDT
Introduction to Responsible AI
Earned sept. 22, 2024 EDT
Generative AI Fundamentals
Earned sept. 22, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned sept. 22, 2024 EDT
Serverless Data Processing with Dataflow: Operations
Earned sept. 20, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned juil. 17, 2024 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned juil. 16, 2024 EDT
Preparing for your Professional Cloud Architect Journey
Earned juil. 16, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned juin 28, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned mai 14, 2024 EDT
Preparing for your Professional Data Engineer Journey
Earned mai 12, 2024 EDT
Gemini for Data Scientists and Analysts
Earned mai 6, 2024 EDT
Essential Google Cloud Infrastructure: Core Services
Earned fév. 25, 2024 EST
Preparing for Your Associate Cloud Engineer Journey
Earned avr. 26, 2023 EDT
Essential Google Cloud Infrastructure: Foundation
Earned mars 1, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned fév. 8, 2023 EST