[DEPRECATED] Data Engineering
Earned Jun 19, 2023 EDT
Cloud Engineering
Earned Jun 19, 2023 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned Jun 19, 2023 EDT
Introduction to Vertex AI Studio
Earned Jun 17, 2023 EDT
Create Image Captioning Models
Earned Jun 17, 2023 EDT
Transformer Models and BERT Model
Earned Jun 17, 2023 EDT
Attention Mechanism
Earned Jun 17, 2023 EDT
Encoder-Decoder Architecture
Earned Jun 17, 2023 EDT
Introduction to Image Generation
Earned Jun 17, 2023 EDT
Generative AI Fundamentals
Earned Jun 17, 2023 EDT
Introduction to Responsible AI
Earned Jun 17, 2023 EDT
Introduction to Generative AI
Earned Jun 17, 2023 EDT
Introduction to Large Language Models
Earned Jun 17, 2023 EDT
BigQuery Basics for Data Analysts
Earned Jun 15, 2020 EDT
Baseline: Infrastructure
Earned Mai 15, 2020 EDT
Google Cloud Essentials
Earned Mai 13, 2020 EDT
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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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 Associate Cloud Engineer 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 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 introduces Vertex AI Studio, a product on Vertex AI, that helps you prototype and customize generative AI models so you can use their capabilities in your applications. In this course, you learn what Vertex AI Studio is, its features and options, and how to use it by walking through demos of the product. In the end, you will have a quiz to test your knowledge.
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This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. 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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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 7 AI principles.
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This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.
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This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps.
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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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If you are a novice cloud developer looking for hands-on practice beyond Google Cloud Essentials, this quest is for you. You will get practical experience through labs that dive into Cloud Storage and other key application services like Stackdriver and Cloud Functions. By taking this quest, you will develop valuable skills that are applicable to any Google Cloud initiative. 1-minute videos walk you through key concepts for these labs.
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In this introductory-level Quest, you will get hands-on practice with the Google Cloud’s fundamental tools and services. Google Cloud Essentials is the recommended first Quest for the Google Cloud learner - you will come in with little or no prior cloud knowledge, and come out with practical experience that you can apply to your first Google Cloud project. From writing Cloud Shell commands and deploying your first virtual machine, to running applications on Kubernetes Engine or with load balancing, Google Cloud Essentials is a prime introduction to the platform’s basic features. 1-minute videos walk you through key concepts for each lab.
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[DEPRECATED] Data Engineering
Earned Jun 19, 2023 EDT
Cloud Engineering
Earned Jun 19, 2023 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned Jun 19, 2023 EDT
Introduction to Vertex AI Studio
Earned Jun 17, 2023 EDT
Create Image Captioning Models
Earned Jun 17, 2023 EDT
Transformer Models and BERT Model
Earned Jun 17, 2023 EDT
Attention Mechanism
Earned Jun 17, 2023 EDT
Encoder-Decoder Architecture
Earned Jun 17, 2023 EDT
Introduction to Image Generation
Earned Jun 17, 2023 EDT
Generative AI Fundamentals
Earned Jun 17, 2023 EDT
Introduction to Responsible AI
Earned Jun 17, 2023 EDT
Introduction to Generative AI
Earned Jun 17, 2023 EDT
Introduction to Large Language Models
Earned Jun 17, 2023 EDT
BigQuery Basics for Data Analysts
Earned Jun 15, 2020 EDT
Baseline: Infrastructure
Earned Mai 15, 2020 EDT
Google Cloud Essentials
Earned Mai 13, 2020 EDT