Build Data Lakes and Data Warehouses on Google Cloud
Earned sept. 12, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned sept. 4, 2022 EDT
Scaling with Google Cloud Operations
Earned août 30, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned août 26, 2022 EDT
Exploring Data Transformation with Google Cloud
Earned août 26, 2022 EDT
Digital Transformation with Google Cloud
Earned août 25, 2022 EDT
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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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.
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This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
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This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects. It explores the various technologies, products, and tools available throughout the data-to-AI lifecycle, empowering data scientists, AI developers, and ML engineers to enhance their expertise through interactive exercises.
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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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In this course, you learn how Gemini, a generative AI-powered collaborator from Google Cloud, helps administrators provision infrastructure. You learn how to prompt Gemini to explain infrastructure, deploy GKE clusters and update existing infrastructure. Using a hands-on lab, you experience how Gemini improves the GKE deployment workflow.
Duet AI was renamed to Gemini, our next-generation model.
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As organizations move their data and applications to the cloud, they must address new security challenges. The Trust and Security with Google Cloud course explores the basics of cloud security, the value of Google Cloud's multilayered approach to infrastructure security, and how Google earns and maintains customer trust in the cloud.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Artificial intelligence (AI) and machine learning (ML) represent an important evolution in information technologies that are quickly transforming a wide range of industries. “Innovating with Google Cloud Artificial Intelligence” explores how organizations can use AI and ML to transform their business processes.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Complete the Evaluate Gen AI model and agent performance skill badge to demonstrate your ability to use the Gen AI evaluation service. You will evaluate models to select the best model for a given task, compare models against each other and evaluate the performance of agents.
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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Evaluation is important at every step of your Gen AI development process. In this course you will learn how to evaluate gen AI agents built using agent frameworks.
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This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production.
Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively. By leveraging Google Cloud's Vertex AI platform, participants will learn how to implement robust evaluation processes for model selection, optimization, and continuous monitoring.
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This lab tests your ability to develop a real-world Generative AI Q&A solution using a RAG framework. You will use Firestore as a vector database and deploy a Flask app as a user interface to query a food safety knowledge base.
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Learn how to create Hybrid Search applications using Vertex AI Vertex Search to combine semantic searching with keyword search to return results based on both semantic meaning and keyword matching.
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Learn how to build your own Retrieval-Augmented Generation (RAG) solutions for greater control and flexibility than out-of-the-box implementations. Create a custom RAG solution using Vertex AI APIs, vector stores, and the LangChain framework.
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This course explores a Retrieval Augmented Generation (RAG) solution in BigQuery to mitigate AI hallucinations. It introduces a RAG workflow that encompasses creating embeddings, searching a vector space, and generating improved answers. The course explains the conceptual reasons behind these steps and their practical implementation with BigQuery. By the end of the course, learners will be able to build a RAG pipeline using BigQuery and generative AI models like Gemini and embedding models to address their own AI hallucination use cases.
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Complete the Edit images with Imagen skill badge to demonstrate your skills with Imagen's mask modes and editing modes to edit images according to certain prompts.
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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This course dives into the world of media creation in Vertex AI using Nano Banana and Veo. Learn to design text and image-based prompts to produce high-quality, consistent images, and captivating, cinematic video clips. You'll also learn to refine generated assets using core editing functions. Finally, this course guides you through multi-tool workflow implementations for creative control and consistency, empowering you to transform images into video clips and leverage Gemini for prompt writing assistance and feedback.
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In this skill bagde, you will demonstrate your ability to use and compare models available in the Vertex AI Model Garden. You'll deploy a model to a Vertex AI Endpoint, query other models via their API, and use Vertex AI's Gen AI evaluation service to measure the performance of multiple models.
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Model tuning is an effective way to customize large models to your tasks. It's a key step to improve the model's quality and efficiency. Model tuning provides benefits such as higher quality results for your specific tasks and increased model robustness. You learn some of the tuning options available in Vertex AI and when to use them.
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Model Garden is a model library that helps you discover, test, and deploy models from Google and Google partners. Learn how to explore the available models and select the right ones for your use case. And how to deploy and interact with Model Garden models through the Google Cloud console and APIs.
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Complete the Extend Gemini with controlled generation and Tool use skill badge to demonstrate your proficiency in connecting models to external tools and APIs. This allows models to augment their knowledge, extend their capabilities and interact with external systems to take actions such as sending an email.
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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An LLM-based application can process language in a way that resembles thought. But if you want to extend its capabilities to take actions by running other functions you have coded, you will need to use function calling. This can also be referred to as tool use. Additionally, you can give a model the ability to search Google or search a data store of documents to ground its responses. In other words, to base its answers on that information. In this course, you’ll explore these concepts.
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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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Do you want to keep your users engaged by suggesting content they'll love?
This course equips you with the skills to build a cutting-edge recommendations app using your own data with no prior machine learning knowledge. You learn to leverage AI Applications to build recommendation applications so that audiences can discover more personalized content, like what to watch or read next, with Google-quality results customized using optimization objectives.
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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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If you've worked with data, you know that some data is more reliable than other data.
In this course, you'll learn a variety of techniques to present the most reliable or useful results to your users. Create serving controls to boost or bury search results. Rank search results to ensure that each query is answered by the most relevant data. If needed, tune your search engine. Learn to measure search results to ensure your search applications deliver the best possible results to each user.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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Initial deployment of Vertex AI Search and Gemini Enterprise apps takes only a few clicks, but getting the configurations right can elevate a deployment from a basic off-the-shelf app to an excellent custom search or recommendations experience.
In this course, you'll learn more about the many ways you can customize and improve search, recommendations, and Gemini Enterprise apps.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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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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Data stores represent a simple way to make content available to many types of generative AI applications, including search applications, recommendations engines, Gemini Enterprise apps, Agent Development Kit agents, and apps built with Google Gen AI or LangChain SDKs. Connect data from many sources include Cloud Storage, Google Drive, chat apps, mail apps, ticketing systems, third-party file storage providers, Salesforce, and many more.
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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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In this course you will learn how Conversational AI Agent Assist can help distill complex customer interactions into concise and clear summaries. Please note Dialogflow CX was recently renamed to Conversational Agents, Virtual agent renamed to Conversational agent, and CCAI Insights were renamed to Conversational Insights, and this course is in the process of being updated to reflect the new product names for Dialogflow CX, and Virtual Agent, CCAI Insights.
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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 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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This course introduces AI Applications. You will learn about the types of apps that you can create using AI Applications, the high-level steps that its data stores automate for you, and what advanced features can be enabled for Search apps.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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AI Applications provides built-in analytics for your Vertex AI Search and Gemini Enterprise apps. Learn what metrics are tracked and how to view them in this course.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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In this skill badge, you will demonstrate your ability to deploy Google Agentspace and set up data stores and actions. To learn these skills, we encourage you to take the course Accelerate Knowledge Exchange with Agentspace.
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Unite Google’s expertise in search and AI with Gemini Enterprise, a powerful tool designed to help employees find specific information from document storage, email, chats, ticketing systems, and other data sources, all from a single search bar. The Gemini Enterprise assistant can also help brainstorm, research, outline documents, and take actions like inviting coworkers to a calendar event to accelerate knowledge work and collaboration of all kinds.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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Earn the intermediate skill badge by completing the Implement CI/CD Pipelines on Google Cloud skill badge course where you learn how to use Artifact Registry, Cloud Build, and Cloud Deploy. You interact with the Google Cloud console, Google Cloud CLI, Cloud Run, and GKE. This course teaches you how to build continuous integration pipelines, store and secure artifacts, scan for vulnerabilities, attest to the validity of approved releases. Additionally, you get hands-on experience deploying applications to both GKE and Cloud Run.
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Gen AI Agents: Transform Your Organization is the fifth and final course of the Gen AI Leader learning path. This course explores how organizations can use custom gen AI agents to help tackle specific business challenges. You gain hands-on practice building a basic gen AI agent, while exploring the components of these agents, such as models, reasoning loops, and tools.
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Transform Your Work With Gen AI Apps is the fourth course of the Gen AI Leader learning path. This course introduces Google’s gen AI applications, such as Google Workspace with Gemini and NotebookLM. It guides you through concepts like grounding, retrieval augmented generation, constructing effective prompts and building automated workflows.
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Gen AI: Navigate the Landscape s 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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Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.
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Learn how Gemini can revolutionize your ability to develop applications! This course helps developers go beyond the basics and learn how to integrate Gemini into their workflows.
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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge course to demonstrate skills in the following: using multimodal prompts to extract information from text and visual data, generating a video description, and retrieving extra information beyond the video using multimodality with Gemini; building metadata of documents containing text and images, getting all relevant text chunks, and printing citations by using Multimodal Retrieval Augmented Generation (RAG) with Gemini.
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This course explores Google Cloud technologies to create and generate embeddings. Embeddings are numerical representations of text, images, video and audio, and play a pivotal role in many tasks that involve the identification of similar items, like Google searches, online shopping recommendations, and personalized music suggestions. Specifically, you’ll use embeddings for tasks like classification, outlier detection, clustering and semantic search.
You’ll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) systems and question-answering solutions, on your own proprietary data using Google Cloud’s Vertex AI.
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This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
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The Google Cloud Computing Foundations courses are for individuals with little to no background or experience in cloud computing. They provide an overview of concepts central to cloud basics, big data, and machine learning, and where and how Google Cloud fits in.
By the end of the series of courses, learners will be able to articulate these concepts and demonstrate some hands-on skills.
The courses should be completed in the following order:
1. Google Cloud Computing Foundations: Cloud Computing Fundamentals
2. Google Cloud Computing Foundations: Infrastructure in Google Cloud
3. Google Cloud Computing Foundations: Networking and Security in Google Cloud
4. Google Cloud Computing Foundations: Data, ML, and AI in Google Cloud
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This course explores Gemini in BigQuery, a suite of AI-driven features to assist data-to-AI workflow. These features include data exploration and preparation, code generation and troubleshooting, and workflow discovery and visualization. Through conceptual explanations, a practical use case, and hands-on labs, the course empowers data practitioners to boost their productivity and expedite the development pipeline.
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Complete the introductory Build a Data Mesh with Dataplex skill badge to demonstrate skills in the following: building a data mesh with Dataplex to facilitate data security, governance, and discovery on Google Cloud. You practice and test your skills in tagging assets, assigning IAM roles, and assessing data quality in Dataplex.
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This course enables system integrators and partners to understand the principles of automated migrations, plan legacy system migrations to Google Cloud leveraging G4 Platform, and execute a trial code conversion.
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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 educates partners on key concepts of Google’s Migrate to Containers. It will cover planning, workload fitness for conversion, deployment with a processing cluster, and the migration process.
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Complete the intermediate Explore Generative AI with the Gemini API in Vertex AI skill badge to demonstrate skills in text generation, image and video analysis for enhanced content creation, and applying function calling techniques within the Gemini API. Discover how to leverage sophisticated Gemini techniques, explore multimodal content generation, and expand the capabilities of your AI-powered projects.
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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 was previously named Multimodal Prompt Engineering with Gemini and PaLM) This course teaches how to use Vertex AI Studio, a Google Cloud console tool for rapidly prototyping and testing generative AI models. You learn to test sample prompts, design your own prompts, and customize foundation models to handle tasks that meet your application's needs. Whether you are looking for text, chat, code, image or speech generative experiences Vertex AI Studio offers you an interface to work with and APIs to integrate your production application.
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Master Google Cloud skills through hands-on labs and friendly competition! Cloud Hero challenges you to conquer a series of Cloud Skills Boost labs, putting your newfound knowledge to practice. Earn points for completing labs accurately, and rack up bonus points for speed. The leaderboard lets you see how you stack up against your peers – can you rise to the top? Remember to click "End" after finishing each lab to claim your well-deserved points.
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Earn a skill badge by completing the Get Started with Looker skill badge course, where you learn how to analyze, visualize, and curate data using Looker Studio and Looker.
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Demonstrate your ability to implement updated prompt engineering techniques and utilize several of Gemini's key capacilities including multimodal understanding and function calling. Then integrate generative AI into a RAG application deployed to Cloud Run. This course contains labs that are to be used as a test environment. They are deployed to test your understanding as a learner with a limited scope. These technologies can be used with fewer limitations in a real world environment.
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By the end of this course, you should be able to articulate Looker's value propositions and what makes it different from other analytics tools in the market. You should also be able to explain how Looker works, and explain the standard components of successful service delivery.
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In this course, you'll use text embeddings for tasks like classification, outlier detection, text clustering and semantic search. You'll combine semantic search with the text generation capabilities of an LLM to build Retrieval Augmented Generation (RAG) solutions, such as for question-answering systems, using Google Cloud's Vertex AI and Google Cloud databases.
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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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Learn which Mandiant products directly enhance or augment capabilities provided by Chronicle SIEM and SOAR and how those products integrate into our workflow.
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Complete the intermediate Develop Serverless Applications on Cloud Run skill badge course to demonstrate skills in the following: integrating Cloud Run with Cloud Storage for data management, architecting resilient asynchronous systems using Cloud Run and Pub/Sub, constructing REST API gateways powered by Cloud Run, and building and deploying services on Cloud Run.
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Complete the intermediate Deploy Kubernetes Applications on Google Cloud skill badge course to demonstrate skills in the following: Configuring and building Docker container images.Creating and managing Google Kubernetes Engine (GKE) clusters.Utilizing kubectl for efficient cluster management.Deploying Kubernetes applications with robust continuous delivery (CD) practices.
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Complete the intermediate Develop Serverless Apps with Firebase skill badge course to demonstrate skills in the following: architecting and building serverless web applications with Firebase, utilizing Firestore for database management, automating deployment processes using Cloud Build, and integrating Google Assistant functionality into your applications.
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This course will provide you with an overview of SIEM technology to set the stage for the differentiation and expansion of capabilities that Chronicle SIEM provides.
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This course will familiarize you with the core functionality of Chronicle, including the user interface, connections, and settings.
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This short course on integrating applications with Gemini 1.0 Pro models on Google Cloud helps you discover the Gemini API and its generative AI models. The course teaches you how to access the Gemini 1.0 Pro and Gemini 1.0 Pro Vision models from code. It lets you test the capabilities of the models with text, image, and video prompts from an app.
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This course introduces the Cloud Run serverless platform for running applications.
In this course, you learn about the fundamentals of Cloud Run, its resource model and the container lifecycle.
You learn about service identities, how to control access to services, and how to develop and test your
application locally before deploying it to Cloud Run. The course also teaches you how to integrate
with other services on Google Cloud so you can build full-featured applications.
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In this course, you learn about containers and how to build, and package container images. The content in this course includes best practices for creating and securing containers, and provides an introduction to Cloud Run and Google Kubernetes Engine for application developers.
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In this course, you learn about Cloud Run functions, Google's serverless, fully-managed functions as a service (FaaS) product that lets you implement single-purpose function code that reponds to HTTP requests and events from your cloud infrastructure.
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This course introduces you to event-based applications and teaches you how to use service orchestration and choreography to coordinate microservices. Using lectures and hands-on labs, you learn how to use Workflows, Eventarc, Cloud Tasks, and Cloud Scheduler to build microservices applications on Google Cloud.
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In this course, you learn the fundamentals of application development on Google Cloud. You learn best practices for cloud applications, and how to select compute and data options to match your application use cases. You're introduced to generative AI and how it's used to help build applications. You learn about authentication and authorization, application deployment, continuous integration and delivery, and monitoring and performance tuning for your applications running in Google Cloud. Using lectures and hands-on labs, you learn how to get started building and running applications on Google Cloud.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to create repeatable deployments by treating infrastructure as code, choose the appropriate application execution environment for an application, and monitor application performance.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
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Complete the intermediate Develop Gen AI Apps with Gemini and Streamlit skill badge course to demonstrate skills in text generation, applying function calls with the Python SDK and Gemini API, and deploying a Streamlit application with Cloud Run. In this course, you learn Gemini prompting, test Streamlit apps in Cloud Shell, and deploy them as Docker containers in Cloud Run.
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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge course to demonstrate skills in the following: creating and managing multi-tenant clusters, monitoring resource usage by namespace, configuring cluster and pod autoscaling for efficiency, setting up load balancing for optimal resource distribution, and implementing liveness and readiness probes to ensure application health and cost-effectiveness.
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In this course, application developers learn how to design and develop cloud-native applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to apply best practices for application development and use the appropriate Google Cloud storage services for object storage, relational data, caching, and analytics.
Completing one version of each lab is required. Each lab is available in Node.js. In most cases, the same labs are also provided in Python or Java. You may complete each lab in whichever language you prefer.
This is the first course of the Developing Applications with Google Cloud series. After completing this course, enroll in the Securing and Integrating Components of your Application course.
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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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Text Prompt Engineering Techniques introduces you to consider different strategic approaches & techniques to deploy when writing prompts for text-based generative AI tasks.
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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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Learn the technical aspects you need to know about Chronicle and how it can help you detect and action threats.
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Course two of the Architecting Hybrid Cloud with Anthos series prepares students to operate and observe Anthos environments. Through presentations and hands-on labs, participants explore adjusting existing clusters, setting up advanced traffic routing policies, securing communication across workloads, and observing clusters in Anthos. This course is a continuation of course one, Multi-Cluster, Multi-Cloud with Anthos, and assumes direct experience with the topics covered in that course.
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Complete the intermediate Implement DevOps Workflows in Google Cloud skill badge to demonstrate skills in the following: creating git repositories with Cloud Source Repositories, launching, managing, and scaling deployments on Google Kubernetes Engine (GKE), and architecting CI/CD pipelines that automate container image builds and deployments to GKE.
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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.
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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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The Google Cloud Rapid Migration & Modernization Program (RaMP) is a holistic, end-to-end migration/modernization program that helps customers & partners leverage expertise and best practices, lower risk, control costs, and simplify a customer's path to cloud success. This course will give an overview of the program and some of the tools and best practices available to support customer migrations & modernizations.
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This quest comprises a series of Challenge and Learning labs aimed at validating your proficiency in setting up a Google Cloud organization for scalable, production-ready, enterprise workloads.
Each lab is structured around the Google Cloud setup checklist, which provides administrators with complete control over a company's Google Cloud resources. The checklist comprises 10 items, and each item includes step-by-step procedures that outline a recommended path to follow. Although there may be multiple ways to complete some tasks, it's important to keep track of your choices if you don't follow the recommended path.
In the Challenge Labs, you'll be given a set of tasks that you must complete on your own with minimal assistance. An automated scoring system will provide feedback on whether you have successfully completed your tasks.
The Learning Labs will provide you with step-by-step instructions on how to perform the required tasks. However, due to limitations imposed by the Qwik…
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The Cloud Foundations Customer Onboarding: Best Practices course enables partners to onboard customers on Google Cloud efficiently and in minimum time, by imparting knowledge, IP, and best practices from the Technical Onboarding Center (TOC) team at Global Delivery Center (GDC). The course explores Cloud Identity and organization, users and groups, administrative access, and resource hierarchy. It also examines network configuration, hybrid connectivity, logging and monitoring, and organizational security.
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This content is deprecated. Please see the latest version of the course, here.
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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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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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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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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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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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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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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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Complete the introductory Monitor and Log with Google Cloud Observability skill badge course to demonstrate skills in the following: monitoring virtual machines in Compute Engine, utilizing Cloud Monitoring for multi-project oversight, extending monitoring and logging capabilities to Cloud Functions, creating and sending custom application metrics, and configuring Cloud Monitoring alerts based on custom metrics.
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Complete the intermediate Build Infrastructure with Terraform on Google Cloud skill badge to demonstrate skills in the following: Infrastructure as Code (IaC) principles using Terraform, provisioning and managing Google Cloud resources with Terraform configurations, effective state management (local and remote), and modularizing Terraform code for reusability and organization.
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Earn a skill badge by completing the Develop your Google Cloud Network skill badge course, where you learn multiple ways to deploy and monitor applications including how to: explore IAM roles and add/remove project access, create VPC networks, deploy and monitor Compute Engine VMs, write SQL queries, deploy and monitor VMs in Compute Engine, and deploy applications using Kubernetes with multiple deployment approaches.
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Earn a skill badge by completing the Set Up an App Dev Environment on Google Cloud skill badge course, where you learn how to build and connect storage-centric cloud infrastructure using the basic capabilities of the following technologies: Cloud Storage, Identity and Access Management, Cloud Functions, and Pub/Sub.
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This course provides an introduction to using Terraform for Google Cloud. It enables learners to describe how Terraform can be used to implement infrastructure as code and to apply some of its key features and functionalities to create and manage Google Cloud infrastructure.
Learners will get hands-on practice building and managing Google Cloud resources using Terraform.
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Welcome to the two-part course on Logging, Monitoring, and Observability in Google Cloud. The core operations tools in Google Cloud break down into two major categories. The operations-focused components and the application performance management tools. This course, Logging and Monitoring in Google Cloud, covers the operations-focused components including Logging, Monitoring, and Service Monitoring. After taking this course, it is suggested that you complete part 2, Observability in Google Cloud, to learn about the available application performance management tools.
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Welcome to the Getting Started with Google Kubernetes Engine course. If you're interested in Kubernetes, a software layer that sits between your applications and your hardware infrastructure, then you’re in the right place! Google Kubernetes Engine brings you Kubernetes as a managed service on Google Cloud.
The goal of this course is to introduce the basics of Google Kubernetes Engine, or GKE, as it’s commonly referred to, and how to get applications containerized and running in Google Cloud. The course starts with a basic introduction to Google Cloud, and is then followed by an overview of containers and Kubernetes, Kubernetes architecture, and Kubernetes operations.
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This accelerated on-demand course introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud. Through a combination of video lectures, demos, and hands-on labs, participants explore and deploy solution elements, including securely interconnecting networks, load balancing, autoscaling, infrastructure automation and managed services.
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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 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.
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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 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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Complete the intermediate Engineer Data for Predictive Modeling with BigQuery ML skill badge to demonstrate skills in the following: building data transformation pipelines to BigQuery using Dataprep by Trifacta; using Cloud Storage, Dataflow, and BigQuery to build extract, transform, and load (ETL) workflows; and building machine learning models using BigQuery ML.
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Complete the intermediate Build a Data Warehouse with BigQuery skill badge course to demonstrate skills in the following: joining data to create new tables, troubleshooting joins, appending data with unions, creating date-partitioned tables, and working with JSON, arrays, and structs in BigQuery.
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Complete the introductory Prepare Data for ML APIs on Google Cloud skill badge to demonstrate skills in the following: cleaning data with Dataprep by Trifacta, running data pipelines in Dataflow, creating clusters and running Apache Spark jobs in Dataproc, and calling ML APIs including the Cloud Natural Language API, Google Cloud Speech-to-Text API, and Video Intelligence API.
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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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In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.
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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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In this course you will get hands-on in order to work through real-world challenges faced when building streaming data pipelines. The primary focus is on managing continuous, unbounded data with Google Cloud products.
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In this intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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Complete the introductory Implementing Cloud Load Balancing for Compute Engine skill badge to demonstrate skills in the following: creating and deploying virtual machines in Compute Engine and configuring network and application load balancers.
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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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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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Organizations of all sizes are embracing the power and flexibility of the cloud to transform how they operate. However, managing and scaling cloud resources effectively can be a complex task.
Scaling with Google Cloud Operations explores the fundamental concepts of modern operations, reliability, and resilience in the cloud, and how Google Cloud can help support these efforts.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Many traditional enterprises use legacy systems and applications that can't stay up-to-date with modern customer expectations. Business leaders often have to choose between maintaining their aging IT systems or investing in new products and services. "Modernize Infrastructure and Applications with Google Cloud" explores these challenges and offers solutions to overcome them by using cloud technology.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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Cloud technology can bring great value to an organization, and combining the power of cloud technology with data has the potential to unlock even more value and create new customer experiences.
“Exploring Data Transformation with Google Cloud” explores the value data can bring to an organization and ways Google Cloud can make data useful and accessible.
Part of the Cloud Digital Leader learning path, this course aims to help individuals grow in their role and build the future of their business.
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There's much excitement about cloud technology and digital transformation, but often many unanswered questions.
For example: What is cloud technology? What does digital transformation mean? How can cloud technology help your organization? Where do you even begin?
If you've asked yourself any of these questions, you're in the right place. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey. If you want to learn about cloud technology so you can excel in your role and help build the future of your business, then this introductory course on digital transformation is for you. This course is part of the Cloud Digital Leader learning path.
Feature Engineering
Earned mars 10, 2026 EDT
Create ML Models with BigQuery ML
Earned mars 3, 2026 EST
Working with Notebooks in Vertex AI
Earned fév. 27, 2026 EST
Introduction to AI and Machine Learning on Google Cloud
Earned fév. 22, 2026 EST
Build intelligent agents with Agent Development Kit (ADK)
Earned oct. 6, 2025 EDT
Gemini for Cloud Architects
Earned sept. 27, 2025 EDT
Trust and Security with Google Cloud
Earned sept. 8, 2025 EDT
Innovating with Google Cloud Artificial Intelligence
Earned sept. 6, 2025 EDT
Evaluate Gen AI model and agent performance
Earned juil. 22, 2025 EDT
Evaluate ADK Agents with Vertex AI Gen AI Evaluation Service
Earned juil. 21, 2025 EDT
Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
Earned juil. 21, 2025 EDT
Deploy a RAG application with vector search in Firestore
Earned juil. 18, 2025 EDT
Implement Hybrid Search
Earned juil. 18, 2025 EDT
Implement RAG with Vertex AI
Earned juil. 17, 2025 EDT
Create Embeddings, Vector Search, and RAG with BigQuery
Earned juil. 11, 2025 EDT
Edit images with Imagen
Earned juil. 5, 2025 EDT
Generate and Edit Media in Vertex AI
Earned juil. 2, 2025 EDT
Deploy and Evaluate Model Garden Models
Earned juin 23, 2025 EDT
Improve Performance by Fine-Tuning Foundation Models
Earned juin 22, 2025 EDT
Find, Explore and Deploy Model Garden Models
Earned juin 22, 2025 EDT
Extend Gemini with controlled generation and Tool use
Earned juin 15, 2025 EDT
Empower Gen AI Apps with Tool Use
Earned juin 15, 2025 EDT
Create media search and media recommendations applications with AI Applications
Earned juin 2, 2025 EDT
Recommendations with AI Applications
Earned juin 2, 2025 EDT
Configure AI Applications to optimize search results
Earned juin 1, 2025 EDT
Improve Vertex AI Search and Gemini Enterprise Search Results
Earned mai 31, 2025 EDT
Vertex AI Search and Gemini Enterprise UI Configurations
Earned mai 29, 2025 EDT
Create and maintain Vertex AI Search data stores
Earned mai 29, 2025 EDT
Create Data Stores for Gen AI Applications
Earned mai 26, 2025 EDT
Deploy Multi-Agent Systems with Agent Development Kit (ADK) and Agent Engine
Earned mai 26, 2025 EDT
Agent Summarization (Custom)
Earned mai 19, 2025 EDT
Extend Gemini Enterprise Assistant Capabilities
Earned mai 18, 2025 EDT
Build search and recommendations applications with AI Applications
Earned mai 18, 2025 EDT
Introduction to AI Applications
Earned mai 18, 2025 EDT
Vertex AI Search and Gemini Enterprise Analytics
Earned mai 17, 2025 EDT
Deploy Google Agentspace
Earned mai 11, 2025 EDT
Accelerate Knowledge Exchange with Gemini Enterprise
Earned mai 11, 2025 EDT
Implement CI/CD Pipelines on Google Cloud
Earned avr. 20, 2025 EDT
Gen AI Agents: Transform Your Organization
Earned avr. 17, 2025 EDT
Gen AI Apps: Transform Your Work
Earned avr. 14, 2025 EDT
Gen AI: Navigate the Landscape
Earned avr. 14, 2025 EDT
Gen AI: Unlock Foundational Concepts
Earned avr. 14, 2025 EDT
Gen AI: Beyond the Chatbot
Earned avr. 12, 2025 EDT
Improving developer velocity with Gemini Code Assist
Earned avr. 7, 2025 EDT
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Earned avr. 3, 2025 EDT
Custom Search with Embeddings in Vertex AI
Earned avr. 1, 2025 EDT
Work with Gemini Models in BigQuery
Earned mars 1, 2025 EST
Google Cloud Computing Foundations: Infrastructure in Google Cloud
Earned mars 1, 2025 EST
Boost Productivity with Gemini in BigQuery
Earned fév. 28, 2025 EST
Build a Data Mesh with Dataplex
Earned fév. 27, 2025 EST
Modernizing Mainframe Applications with Google Cloud
Earned fév. 18, 2025 EST
Vector Search and Embeddings
Earned fév. 8, 2025 EST
Migrate to Containers using the Migration Assessment Tool
Earned fév. 1, 2025 EST
Explore Generative AI with the Gemini API in Vertex AI
Earned jan. 24, 2025 EST
Introduction to Data Engineering on Google Cloud
Earned jan. 17, 2025 EST
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned jan. 13, 2025 EST
Core Infrastructure I
Earned déc. 12, 2024 EST
Get Started with Looker
Earned nov. 15, 2024 EST
Build and Deploy a Generative AI solution using a RAG framework
Earned nov. 13, 2024 EST
Looker Explained
Earned nov. 12, 2024 EST
Develop Advanced Enterprise Search and Conversation Applications
Earned nov. 9, 2024 EST
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned nov. 2, 2024 EDT
Mandiant Fundamentals
Earned oct. 22, 2024 EDT
Develop Serverless Applications on Cloud Run
Earned oct. 19, 2024 EDT
Deploy Kubernetes Applications on Google Cloud
Earned oct. 15, 2024 EDT
Develop Serverless Apps with Firebase
Earned oct. 14, 2024 EDT
Chronicle SIEM Fundamentals
Earned oct. 14, 2024 EDT
[DEPRECATED] SOAR Fundamentals
Earned oct. 8, 2024 EDT
Integrating Applications with Gemini 1.0 Pro on Google Cloud
Earned sept. 29, 2024 EDT
Developing Applications with Cloud Run on Google Cloud: Fundamentals
Earned sept. 29, 2024 EDT
Developing Containerized Applications on Google Cloud
Earned sept. 22, 2024 EDT
Developing Applications with Cloud Run Functions on Google Cloud
Earned sept. 20, 2024 EDT
Service Orchestration and Choreography on Google Cloud
Earned sept. 1, 2024 EDT
Developing Applications with Google Cloud: Foundations
Earned août 26, 2024 EDT
App Deployment, Debugging, and Performance
Earned août 20, 2024 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned août 14, 2024 EDT
Optimize Costs for Google Kubernetes Engine
Earned juil. 28, 2024 EDT
Getting Started With Application Development
Earned mai 26, 2024 EDT
Preparing for your Professional Data Engineer Journey
Earned mai 5, 2024 EDT
Text Prompt Engineering Techniques
Earned avr. 23, 2024 EDT
Introduction to Vertex AI Studio
Earned avr. 22, 2024 EDT
Security Practices with Google Security Operations - SIEM
Earned avr. 7, 2024 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos
Earned mars 23, 2024 EDT
Implement DevOps Workflows in Google Cloud
Earned fév. 11, 2024 EST
Logging and Monitoring in Google Cloud
Earned fév. 9, 2024 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned jan. 24, 2024 EST
Developing a Google SRE Culture
Earned jan. 14, 2024 EST
Rapid Migration & Modernization Program
Earned jan. 7, 2024 EST
Technical Onboarding Checklist Certification Quest
Earned nov. 27, 2023 EST
Cloud Foundations: Customer Onboarding Best Practices
Earned nov. 19, 2023 EST
Generative AI Explorer : Vertex AI
Earned oct. 31, 2023 EDT
Introduction to Image Generation
Earned oct. 23, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned oct. 23, 2023 EDT
Generative AI Fundamentals
Earned oct. 12, 2023 EDT
Introduction to Responsible AI
Earned oct. 12, 2023 EDT
Introduction to Large Language Models
Earned oct. 12, 2023 EDT
Introduction to Generative AI
Earned oct. 11, 2023 EDT
Generative AI for Business Leaders
Earned oct. 10, 2023 EDT
Monitor and Log with Google Cloud Observability
Earned oct. 1, 2023 EDT
Build Infrastructure with Terraform on Google Cloud
Earned sept. 25, 2023 EDT
Develop Your Google Cloud Network
Earned sept. 24, 2023 EDT
Set Up an App Dev Environment on Google Cloud
Earned sept. 22, 2023 EDT
Getting Started with Terraform for Google Cloud
Earned sept. 21, 2023 EDT
Logging and Monitoring in Google Cloud
Earned sept. 9, 2023 EDT
Getting Started with Google Kubernetes Engine
Earned juil. 8, 2023 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned juin 13, 2023 EDT
Essential Google Cloud Infrastructure: Core Services
Earned juin 2, 2023 EDT
Essential Google Cloud Infrastructure: Foundation
Earned mai 7, 2023 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned mai 1, 2023 EDT
Preparing for Your Associate Cloud Engineer Journey
Earned mars 30, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned fév. 19, 2023 EST
Engineer Data for Predictive Modeling with BigQuery ML
Earned fév. 9, 2023 EST
Build a Data Warehouse with BigQuery
Earned fév. 4, 2023 EST
Prepare Data for ML APIs on Google Cloud
Earned jan. 30, 2023 EST
Serverless Data Processing with Dataflow: Operations
Earned jan. 28, 2023 EST
Serverless Data Processing with Dataflow: Develop Pipelines
Earned jan. 14, 2023 EST
Serverless Data Processing with Dataflow: Foundations
Earned déc. 5, 2022 EST
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned déc. 3, 2022 EST
Build Streaming Data Pipelines on Google Cloud
Earned nov. 20, 2022 EST
Build Batch Data Pipelines on Google Cloud
Earned oct. 10, 2022 EDT
Implementing Cloud Load Balancing for Compute Engine
Earned sept. 14, 2022 EDT
Build Data Lakes and Data Warehouses on Google Cloud
Earned sept. 12, 2022 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned sept. 4, 2022 EDT
Scaling with Google Cloud Operations
Earned août 30, 2022 EDT
Modernize Infrastructure and Applications with Google Cloud
Earned août 26, 2022 EDT
Exploring Data Transformation with Google Cloud
Earned août 26, 2022 EDT
Digital Transformation with Google Cloud
Earned août 25, 2022 EDT