Accelerate 2019: Google Cloud SCORE... PreGame!
Earned sty 20, 2019 EST
In this course, you’ll learn to use the Google Agent Development Kit to build complex, multi-agent systems. You will build agents equipped with tools, and connect them with parent-child relationships and flows to define how they interact. You’ll run your agents locally and deploy them to Vertex AI Agent Engine to run as a managed agentic flow, with infrastructure decisions and resource scaling handled by Agent Engine.
Please note these labs are based off a pre-released version of this product. There may be some lag on these labs as we provide maintenance updates.
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This structured course is for developers interested in building intelligent agents using the Agent Development Kit (ADK). It combines hands-on experience, core concepts, and practical application, to provide a comprehensive guide to using ADK. You can also join our community of Google Cloud experts and peers to ask questions, collaborate on answers, and connect with the Googlers making the products you use every day.
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Complete the Create media search and media recommendations applications with AI Applications skill badge to demonstrate your ability to create, configure, and access media search and recommendations applications using AI Applications.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
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 the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Complete the Extend Gemini Enterprise Assistant Capabilities skill badge to demonstrate your ability to extend Gemini Enterprise assistant's capabilities with actions, grounding with Google Search, and a conversational agent.
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 the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Complete the Configure AI Applications to optimize search results skill badge to demonstrate your proficiency in configuring search results from AI Applications. You will be tasked with implementing search serving controls to boost and bury results, filter entries from search results and display metadata in your search interface.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
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 the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Complete the Create and maintain Vertex AI Search data stores skill badge to demonstrate your proficiency in building various types of data stores used in Vertex AI Search applications.
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 the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Complete the Build search and recommendations AI Applications skill badge to demonstrate your proficiency in deploying search and recommendation applications through AI Applications. Additionally, emphasis is placed on constructing a tailored Q&A system utilizing data stores.
Please note that AI Applications was previously named Agent Builder, so you may encounter this older name within the lab content.
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 the assessment challenge lab, to receive a skill badge that you can share with your network.
When you complete this course, you can earn the badge displayed here and claim it on Credly! Boost your cloud career by showing the world the skills you have developed!
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Na tym szkoleniu przedstawiamy koncepcje interpretowalności i przejrzystości AI. Omawiamy na nim, jak ważna jest przejrzystość AI dla deweloperów i inżynierów. Pokazujemy praktyczne techniki i narzędzia, które pomagają osiągnąć interpretowalność oraz przejrzystość zarówno w danych, jak i modelach AI.
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Na tym szkoleniu przedstawiamy koncepcje odpowiedzialnej AI i zasad dotyczących AI. Omawiamy praktyczne metody identyfikowania obiektywności i uprzedzeń, a także ograniczania występowania uprzedzeń podczas używania AI/ML. W trakcie szkolenia przedstawiamy też praktyczne techniki i narzędzia, które umożliwiają wdrożenie sprawdzonych metod w zakresie odpowiedzialnej AI przy użyciu usług Google Cloud oraz narzędzi open source.
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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine.
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This course is dedicated to equipping you with the knowledge and tools needed to uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and exploring how Vertex AI empowers AI teams to streamline MLOps processes and achieve success in Generative AI projects.
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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.
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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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In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine.
This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.
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This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.
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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.
Learners will get hands-on practice using Vertex AI Feature Store's streaming ingestion at the SDK layer.
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This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
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This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to find which data columns make the most useful features. This course also includes content and labs on feature engineering using BigQuery ML, Keras, and TensorFlow.
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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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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 takes a real-world approach to the ML Workflow through a case study. An ML team faces several ML business requirements and use cases. The team must understand the tools required for data management and governance and consider the best approach for data preprocessing.
The team is presented with three options to build ML models for two use cases. The course explains why they would use AutoML, BigQuery ML, or custom training to achieve their objectives.
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This course covers building ML models with TensorFlow and Keras, improving the accuracy of ML models and writing ML models for scaled use.
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The Generative AI Explorer - Vertex Quest is a collection of labs on how to use Generative AI on Google Cloud. Through the labs, you will learn about how to use the models in the Vertex AI PaLM API family, including text-bison, chat-bison, and textembedding-gecko. You will also learn about prompt design, best practices, and how it can be used for ideation, text classification, text extraction, text summarization, and more. You will also learn how to tune a foundation model by training it via Vertex AI custom training and deploy it to a Vertex AI endpoint.
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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 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 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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Aby zdobyć odznakę umiejętności, ukończ szkolenia Introduction to Generative AI, Introduction to Large Language Models i Introduction to Responsible AI. Zdaj test i pokaż, że rozumiesz podstawowe koncepcje związane z generatywną AI.
Odznaka umiejętności to cyfrowa odznaka wydawana przez Google Cloud, która potwierdza Twoją wiedzę o produktach i usługach Google Cloud. Ustaw swój profil jako publiczny i dodaj odznakę umiejętności do profilu w mediach społecznościowych, aby pochwalić się swoim osiągnięciem.
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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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Celem tego szybkiego szkolenia dla początkujących jest wyjaśnienie, czym jest odpowiedzialna AI i dlaczego jest ważna, oraz przedstawienie, jak Google wprowadza ją w swoich usługach. Szkolenie zawiera także wprowadzenie do siedmiu zasad Google dotyczących sztucznej inteligencji.
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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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To szybkie szkolenie dla początkujących wyjaśnia, czym są duże modele językowe (LLM) oraz jakie są ich zastosowania. Przedstawia również możliwości zwiększenia ich wydajności przez dostrajanie przy użyciu promptów oraz narzędzia Google, które pomogą Ci tworzyć własne aplikacje korzystające z generatywnej AI.
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Celem tego szybkiego szkolenia dla początkujących jest wyjaśnienie, czym jest generatywna AI oraz jakie są jej zastosowania. Szkolenie przedstawia również różnice pomiędzy tą technologią a tradycyjnymi systemami uczącymi się, a także narzędzia Google, które pomogą Ci tworzyć własne aplikacje korzystające z generatywnej AI.
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If you are a Technical Attendee at Accelerate 2020 Las Vegas (Jan 14-16, 2020), enroll now and complete this Quest. You'll be part of a hands-on labs competition, Google Cloud Hero, where you'll take labs to score points and compete with your colleagues to win prizes. The six labs in this Quest are the prerequisites: you need to complete these before attending, and the points you earn for completing these labs will be part of your event score. In order to maximize your final score and have a chance to be on the leaderboard, you must complete these labs!
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Security is an uncompromising feature of Google Cloud services, and Google Cloud has developed specific tools for ensuring safety and identity across your projects. In this fundamental-level quest, you will get hands-on practice with Google Cloud’s Identity and Access Management (IAM) service, which is the go-to for managing user and virtual machine accounts. You will get experience with network security by provisioning VPCs and VPNs, and learn what tools are available for security threat and data loss protections.
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If you are a GCCT Technical Attendee at Accelerate 2019 Las Vegas (Jan 28-31, 2019), enroll now and complete this Quest. You'll be part of an on-site competition, Google Cloud SCORE, where you'll take hands-on labs to score points and compete with your colleagues to win prizes. The five labs in this Quest are the prerequisites: you need to complete these before attending, and the points you earn for completing these labs will be part of your event score. In order to maximize your final score and have a chance to be on the leaderboard, you must complete these labs!
Deploy Multi-Agent Systems with Agent Development Kit (ADK) and Agent Engine
Earned sie 5, 2025 EDT
Build intelligent agents with Agent Development Kit (ADK)
Earned sie 5, 2025 EDT
Create media search and media recommendations applications with AI Applications
Earned maj 16, 2025 EDT
Extend Gemini Enterprise Assistant Capabilities
Earned maj 16, 2025 EDT
Configure AI Applications to optimize search results
Earned maj 15, 2025 EDT
Create and maintain Vertex AI Search data stores
Earned maj 14, 2025 EDT
Build search and recommendations applications with AI Applications
Earned maj 13, 2025 EDT
Odpowiedzialna AI dla deweloperów: interpretowalność i przejrzystość
Earned maj 13, 2024 EDT
Odpowiedzialna AI dla deweloperów: obiektywność i uprzedzenia
Earned maj 12, 2024 EDT
Vector Search and Embeddings
Earned maj 12, 2024 EDT
Machine Learning Operations (MLOps) for Generative AI
Earned maj 4, 2024 EDT
Prompt Design in Vertex AI
Earned kwi 21, 2024 EDT
Introduction to Vertex AI Studio
Earned kwi 11, 2024 EDT
Recommendation Systems on Google Cloud
Earned lut 19, 2024 EST
Natural Language Processing on Google Cloud
Earned lut 15, 2024 EST
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned lut 8, 2024 EST
Production Machine Learning Systems
Earned lut 8, 2024 EST
Feature Engineering
Earned lut 6, 2024 EST
Machine Learning Operations (MLOps): Getting Started
Earned lut 6, 2024 EST
Launching into Machine Learning
Earned lut 5, 2024 EST
Machine Learning in the Enterprise
Earned lut 4, 2024 EST
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned lut 2, 2024 EST
Generative AI Explorer - Vertex AI
Earned wrz 6, 2023 EDT
How Google Does Machine Learning
Earned sie 1, 2023 EDT
Introduction to Vertex AI Studio
Earned cze 13, 2023 EDT
Create Image Captioning Models
Earned cze 13, 2023 EDT
Transformer Models and BERT Model
Earned cze 12, 2023 EDT
Generative AI Fundamentals - Polski
Earned cze 12, 2023 EDT
Encoder-Decoder Architecture
Earned cze 11, 2023 EDT
Introduction to Image Generation
Earned cze 3, 2023 EDT
Introduction to Responsible AI - Polski
Earned cze 3, 2023 EDT
Attention Mechanism
Earned cze 2, 2023 EDT
Introduction to Large Language Models - Polski
Earned cze 1, 2023 EDT
Introduction to Generative AI - Polski
Earned maj 31, 2023 EDT
Accelerate 2020: Google Cloud Hero... PreGame!
Earned sty 10, 2020 EST
Security & Identity Fundamentals
Earned kwi 1, 2019 EDT
Accelerate 2019: Google Cloud SCORE... PreGame!
Earned sty 20, 2019 EST