Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Jul 5, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned Jul 4, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned Jun 28, 2024 EDT
Vector Search and Embeddings
Earned Jun 24, 2024 EDT
Intro to CCAI and CCAI Engagement Framework
Earned Jun 17, 2024 EDT
Getting Started with MongoDB Atlas on Google Cloud
Earned Jun 17, 2024 EDT
Getting Started with the Vertex AI Gemini API
Earned Jun 15, 2024 EDT
Multimodality with Gemini
Earned Jun 15, 2024 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned Jun 14, 2024 EDT
Prompt Design in Vertex AI
Earned Jun 3, 2024 EDT
Implement DevOps Workflows in Google Cloud
Earned May 15, 2024 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos
Earned Apr 10, 2024 EDT
Developing a Google SRE Culture
Earned Apr 3, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned Mar 24, 2024 EDT
Build and Deploy Machine Learning Solutions on Vertex AI
Earned Mar 19, 2024 EDT
ML Pipelines on Google Cloud
Earned Mar 11, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Dec 11, 2023 EST
Machine Learning Operations (MLOps): Getting Started
Earned Dec 9, 2023 EST
Recommendation Systems on Google Cloud
Earned Dec 5, 2023 EST
Natural Language Processing on Google Cloud
Earned Nov 6, 2023 EST
Computer Vision Fundamentals with Google Cloud
Earned Oct 25, 2023 EDT
Generative AI Explorer : Vertex AI
Earned Oct 25, 2023 EDT
Generative AI for Business Leaders
Earned Oct 25, 2023 EDT
Production Machine Learning Systems
Earned Oct 22, 2023 EDT
Machine Learning in the Enterprise
Earned Oct 19, 2023 EDT
Feature Engineering
Earned Oct 16, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Oct 13, 2023 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned Oct 11, 2023 EDT
Data Warehousing for Partners: Analyze Data with Looker
Earned Oct 10, 2023 EDT
Analyzing and Visualizing Data the Google Way
Earned Oct 10, 2023 EDT
Delivery Shadowing
Earned Oct 9, 2023 EDT
Case Studies
Earned Oct 9, 2023 EDT
Technology + Beyond the UI
Earned Oct 9, 2023 EDT
Technology + Within the UI
Earned Oct 8, 2023 EDT
Table Calculations, Pivots, and Visualizations
Earned Oct 8, 2023 EDT
Building Reports in Looker
Earned Oct 8, 2023 EDT
Liquid Templates and Parameters
Earned Oct 6, 2023 EDT
Extends to Keep LookML DRY
Earned Oct 5, 2023 EDT
Admin Roles and Folder Access
Earned Oct 5, 2023 EDT
Version Control and Caching
Earned Oct 5, 2023 EDT
The Modern Data Platform and LookML
Earned Oct 5, 2023 EDT
Driving Data Culture and Designing Dashboards
Earned Oct 3, 2023 EDT
Achieving Business Outcomes with Looker
Earned Oct 2, 2023 EDT
Looker Explained
Earned Oct 2, 2023 EDT
Create ML Models with BigQuery ML
Earned Sep 11, 2023 EDT
Data Catalog Fundamentals
Earned Sep 11, 2023 EDT
Manage Data Models in Looker
Earned Sep 11, 2023 EDT
Build LookML Objects in Looker
Earned Sep 1, 2023 EDT
Derive Insights from BigQuery Data
Earned Aug 29, 2023 EDT
Applying Advanced LookML Concepts in Looker
Earned Aug 28, 2023 EDT
Understanding LookML in Looker
Earned Aug 25, 2023 EDT
Prepare Data for Looker Dashboards and Reports
Earned Aug 7, 2023 EDT
Developing Data Models with LookML
Earned Jul 24, 2023 EDT
Generative AI Fundamentals
Earned Jul 21, 2023 EDT
Search with AI Applications
Earned Jul 19, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Jul 18, 2023 EDT
Applying Machine Learning to your Data with Google Cloud
Earned Jul 15, 2023 EDT
Achieving Advanced Insights with BigQuery
Earned Jul 14, 2023 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Jul 7, 2023 EDT
Exploring and Preparing your Data with BigQuery
Earned Jul 3, 2023 EDT
Launching into Machine Learning
Earned Jul 2, 2023 EDT
Text Prompt Engineering Techniques
Earned Jun 27, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned Jun 26, 2023 EDT
Implementing Generative AI with Vertex AI
Earned Jun 25, 2023 EDT
Generative AI Explorer - Vertex AI
Earned Jun 20, 2023 EDT
Introduction to Vertex AI Studio
Earned Jun 19, 2023 EDT
How Google Does Machine Learning
Earned Jun 19, 2023 EDT
Create Image Captioning Models
Earned Jun 16, 2023 EDT
Transformer Models and BERT Model
Earned Jun 15, 2023 EDT
Encoder-Decoder Architecture
Earned Jun 15, 2023 EDT
Attention Mechanism
Earned Jun 15, 2023 EDT
Introduction to Image Generation
Earned Jun 15, 2023 EDT
Generative AI Fundamentals
Earned Jun 15, 2023 EDT
Introduction to Responsible AI
Earned Jun 15, 2023 EDT
Introduction to Large Language Models
Earned Jun 15, 2023 EDT
Introduction to Generative AI
Earned Jun 15, 2023 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Jun 13, 2023 EDT
Serverless Data Processing with Dataflow: Operations
Earned Jun 13, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 11, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Jun 6, 2023 EDT
Build a Data Warehouse with BigQuery
Earned Jun 5, 2023 EDT
Prepare Data for ML APIs on Google Cloud
Earned May 31, 2023 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned May 31, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Apr 25, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Apr 24, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned Apr 21, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned Apr 17, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Apr 5, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Mar 28, 2023 EDT
Preparing for your Professional Cloud Architect Journey
Earned Mar 6, 2023 EST
Certification Learning Path: Professional Cloud Architect
Earned Feb 20, 2023 EST
Set Up a Google Cloud Network
Earned Feb 9, 2023 EST
Optimize Costs for Google Kubernetes Engine
Earned Jan 27, 2023 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned Jan 25, 2023 EST
Build Infrastructure with Terraform on Google Cloud
Earned Jan 18, 2023 EST
Develop Your Google Cloud Network
Earned Jan 17, 2023 EST
Set Up an App Dev Environment on Google Cloud
Earned Jan 16, 2023 EST
Implementing Cloud Load Balancing for Compute Engine
Earned Jan 15, 2023 EST
Getting Started with Terraform for Google Cloud
Earned Jan 15, 2023 EST
Logging and Monitoring in Google Cloud
Earned Jan 13, 2023 EST
Getting Started with Google Kubernetes Engine
Earned Jan 10, 2023 EST
Essential Google Cloud Infrastructure: Core Services
Earned Jan 9, 2023 EST
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned Jan 8, 2023 EST
Essential Google Cloud Infrastructure: Foundation
Earned Jan 5, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned Jan 4, 2023 EST
Preparing for Your Associate Cloud Engineer Journey
Earned Jan 3, 2023 EST
Scaling with Google Cloud Operations
Earned Dec 29, 2022 EST
Modernize Infrastructure and Applications with Google Cloud
Earned Dec 29, 2022 EST
Exploring Data Transformation with Google Cloud
Earned Dec 28, 2022 EST
Digital Transformation with Google Cloud
Earned Dec 27, 2022 EST
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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This course reviews the essential security features of Model Armor and equips you to work with the service. You’ll learn about the security risks associated with LLMs and how Model Armor protects your AI applications.
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Generate engaging media with Google's foundation models for media. Create new images with Imagen, or edit your existing photos by adding details or outpainting to create a wider view. Replace backgrounds to put your products in new scenes. And learn the basics of generating videos with Veo!
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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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Learn a variety of strategies and techniques to engineer effective prompts for generative models
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Learn how to leverage Gemini multimodal capabilities to process and generate text, images, and audio and to integrate Gemini through APIs to perform tasks such as content creation and summarization.
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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 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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NotebookLM is an AI-powered collaborator that helps you do your best thinking. After uploading your documents, NotebookLM becomes an instant expert in those sources so you can read, take notes, and collaborate with it to refine and organize your ideas.
NotebookLM Pro gives you everything already included with NotebookLM, as well as higher utilization limits, access to premium features, and additional sharing options and analytics.
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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.
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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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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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Data stores represent a simple way to make content available to many types of generative AI applications, including search applications, recommendations engines, Google Agentspace 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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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.
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AI Applications provides built-in analytics for your Vertex AI Search and Google Agentspace apps. Learn what metrics are tracked and how to view them in this course.
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Initial deployment of Vertex AI Search and Google Agentspace 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 Google Agentspace apps.
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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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Complete the Extend Google Agentspace assistant capabilities skill badge to demonstrate your ability to extend Google Agentspace 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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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 Google Agentspace, an enterprise 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 Google Agentspace 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.
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In this course, you'll learn to develop generative agents that answer questions using websites, documents, or structured data. You will explore Vertex AI Applications and understand the advantages of data store agents, including their scalability and security.
You'll learn about different data store types and also discover how to connect data stores to agents and add personalization for enhanced responses.
Finally, you'll gain insights into common search configurations and troubleshooting techniques.
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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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In this course you will learn the key architectural considerations that need to be taken into account when designing for the implementation of Conversational AI solutions. Please note Dialogflow CX was recently renamed to Conversational Agents and CCAI Insights was renamed to Conversational Insights.
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This course equips app developers with the skills to integrate generative AI features into their applications using Firebase Genkit. You learn how to leverage Firebase Genkit's capabilities for backend flows and seamless model execution, all using Node.js. The course guides you through the entire process, from prototyping to production, providing a pattern for building next-generation AI-powered applications.
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Learn to build generative AI applications leveraging Firebase Genkit to call LLMs on Google Cloud and elsewhere, simplify complex applications' code and deploy your solution on Google Cloud.
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All applications, including generative AI applications, should be deployed securely & have their performance monitored. In this course, you will explore a pattern for easily securing prototype generative AI applications for internal tool use or customer demos. Additionally, you will learn strategies to unit test generative AI applications and evaluate their performance with the Rapid Evaluation API.
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Learn to use LangChain to call Google Cloud LLMs and Generative AI Services and Datastores to simplify complex applications' code.
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This course equips full-stack mobile and web developers with the skills to integrate generative AI features into their applications using LangChain. You'll learn how to leverage LangChain’s capabilities for backend flows and seamless model execution, all within the familiar environment of Python. The course guides you through the entire process, from prototyping to production, ensuring a smooth journey in building next-generation AI-powered applications.
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Generative AI applications can create new user experiences that were nearly impossible before the invention of large language models (LLMs). As an application developer, how can you use generative AI to build engaging, powerful apps on Google Cloud?
In this course, you'll learn about generative AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs. You'll learn about a production-ready architecture that can be used for generative AI applications and you'll build an LLM and RAG-based chat application.
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Complete the intermediate Inspect Rich Documents with Gemini Multimodality and Multimodal RAG skill badge 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. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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This is an introductory course to all solutions in the Conversational AI portfolio and the Gen AI features that are available to transform them. The course also explores the business case around Conversational AI, and the use cases and user personas addressed by the solution. Please note Dialogflow CX was recently renamed to Conversational Agents and this course is in the process of being updated to reflect the new product name for Dialogflow CX.
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Demonstrate the ability to create and deploy deterministic virtual agents using Dialgflow CX and augment responses by grounding results on your own data integrating with Vertex AI Agent Builder data stores and leveraging Gemini for summarizations.
You will use the following technologies and Google Cloud services:
Vertex AI Agent Builder
Dialogflow CX
Gemini
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This quest will test your ability to create and deploy both Search applications and Chatbots using Vertex AI Agent Builder and Dialgflow. You will also be tasked with implementing a custom RAG system that uses the Discovery API to query a Vertex AI Data Store and use Gemini to answer user questions.
You will use the following technologies and Google Cloud services:
Vertex AI Agent Builder
Search Apps
Agents
Gemini
The assessment is divided into three main tasks:
Building and deploying a Website Search App
Deploying a Chatbot built using Vertex AI Agent Builder
Creating a Custom Q&A Solution using the Discovery API
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This course is for Partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities, learn to identify high-impact use cases, and develop the skills to demonstrate and integrate these technologies seamlessly into client solutions and operations.
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This course is for Partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases.
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This course is for Google Cloud’s top partner sellers and technical pre-sales engineers to gain a comprehensive understanding of Google Cloud's cutting-edge Generative AI capabilities and learn to identify high-impact use cases. Those who complete the training and assessment will receive the Google Cloud Generative AI Trailblazer badge through Skills Boost.
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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 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 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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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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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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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 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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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 is a introductory course to all solutions in the Contact Centre AI (CCAI) portfolio and the Generative AI features that are poised to transform them. The course also explores the CCAI go to market and engagement model, the business case around CCAI, as well as the use cases and user personas addressed by the solution.
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MongoDB Atlas provided customers a fully managed, database-as-a-service on Google’s data cloud that is unmatched in speed, scale, and security—all with AI built in. Modern database systems, including MongoDB, have been a big step forward—giving businesses a more flexible, scalable, and developer-friendly alternative to legacy relational databases. But there is an even bigger payoff with a solution such as MongoDB Atlas a fully managed, database-as-a-service (DBaaS) offering. It is an approach that gives businesses all of the advantages of a modern, scalable, highly available database, while freeing IT to focus on high-value activities.
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Get hands-on with the Gemini Pro and Gemini Pro Vision models through our new labs. This course gives you a unique chance to explore these powerful AI tools while our training content is still in development. Learn to interact with the models using the Vertex AI Gemini API and cURL commands, and help us create the best possible learning experience around this technology.
Important Disclaimer: Please note that these labs are under active development. Functionality may occasionally change or break unexpectedly, and content might be removed or altered without notice. By proceeding with this course, you acknowledge this potential disruption.
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Delve into the power of multimodal AI with this project-based course using Gemini. Master essential techniques and build advanced applications. You will:
- Experiment with multimodal use cases to expand application possibilities
- Implement recommendation systems that combine suggestions with clear reasoning
- Design a powerful document search engine using multimodal RAG methods
Important Disclaimer: Please note that these labs are under active development. Functionality may occasionally change or break unexpectedly, and content might be removed or altered without notice. By proceeding with this course, you acknowledge this potential disruption.
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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 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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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. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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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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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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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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Earn the intermediate skill badge by completing the Build and Deploy Machine Learning Solutions on Vertex AI skill badge course, where you learn how to use Google Cloud's Vertex AI platform, AutoML, and custom training services to train, evaluate, tune, explain, and deploy machine learning models.
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In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
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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 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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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 describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models.
The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.
The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models.
Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.
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This content is deprecated. Please see the latest version of the course, here.
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A Business Leader in Generative AI can articulate the capabilities of core cloud Generative AI products and services and understand how they benefit organizations. This course provides an overview of the types of opportunities and challenges that companies often encounter in their digital transformation journey and how they can leverage Google Cloud's generative AI products to overcome these challenges.
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This 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 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 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 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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This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
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This course explores how to leverage Looker to create data experiences and gain insights with modern business intelligence (BI) and reporting.
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This learning experience guides you through the process of utilizing various data sources and multiple Google Cloud products (including BigQuery and Google Sheets using Connected Sheets) to analyze, visualize, and interpret data to answer specific questions and share insights with key decision makers.
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In this course, you shadow a series of client meetings led by a Looker Professional Services Consultant.
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By the end of this course, you should feel confident employing technical concepts to fulfill business requirements and be familiar with common complex design patterns.
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In this course you will discover additional tools for your toolbox for working with complex deployments, building robust solutions, and delivering even more value.
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Develop technical skills beyond LookML along with basic administration for optimizing Looker instances
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This course reviews the processes for creating table calculations, pivots and visualizations
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This course is designed for Looker users who want to create their own ad-hoc reports. It assumes experience of everything covered in our Get Started with Looker course (logging in, finding Looks & dashboards, adjusting filters, and sending data)
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In this course you will discover Liquid, the templating language invented by Shopify and explore how it can be used in Looker to create dynamic links, content, formatting, and more.
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Hands on course covering the main uses of extends and the three primary LookML objects extends are used on as well as some advanced usage of extends.
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This course is designed to teach you about roles, permission sets and model sets. These are areas that are used together to manage what users can do and what they can see in Looker.
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This course aims to introduce you to the basic concepts of Git: what it is and how it's used in Looker. You will also develop an in-depth knowledge of the caching process on the Looker platform, such as why they are used and why they work
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This course provides an introduction to databases and summarized the differences in the main database technologies. This course will also introduce you to Looker and how Looker scales as a modern data platform. In the lessons, you will build and maintain standard Looker data models and establish the foundation necessary to learn Looker's more advanced features.
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This course provides an iterative approach to plan, build, launch, and grow a modern, scalable, mature analytics ecosystem and data culture in an organization that consistently achieves established business outcomes. Users will also learn how to design and build a useful, easy-to-use dashboard in Looker. It assumes experience with everything covered in our Getting Started with Looker and Building Reports in Looker courses.
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In this course, we’ll show you how organizations are aligning their BI strategy to most effectively achieve business outcomes with Looker. We'll follow four iterative steps: Plan, Build, Launch, Grow, and provide resources to take into your own services delivery to build Looker with the goal of achieving business outcomes.
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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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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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Data Catalog is deprecated and will be discontinued on January 30, 2026. You can still complete this course if you want to. For steps to transition your Data Catalog users, workloads, and content to Dataplex Catalog, see Transition from Data Catalog to Dataplex Catalog (https://cloud.google.com/dataplex/docs/transition-to-dataplex-catalog). Data Catalog is a fully managed and scalable metadata management service that empowers organizations to quickly discover, understand, and manage all of their data. In this quest you will start small by learning how to search and tag data assets and metadata with Data Catalog. After learning how to build your own tag templates that map to BigQuery table data, you will learn how to build MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors.
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Complete the intermediate Manage Data Models in Looker skill badge to demonstrate skills in the following: maintaining LookML project health; utilizing SQL runner for data validation; employing LookML best practices; optimizing queries and reports for performance; and implementing persistent derived tables and caching policies. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a digital badge that you can share with your network.
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Complete the introductory Build LookML Objects in Looker skill badge course to demonstrate skills in the following: building new dimensions and measures, views, and derived tables; setting measure filters and types based on requirements; updating dimensions and measures; building and refining Explores; joining views to existing Explores; and deciding which LookML objects to create based on business requirements.
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Complete the introductory Derive Insights from BigQuery Data skill badge to demonstrate skills in the following: write SQL queries, query public tables, load sample data into BigQuery, troubleshoot common syntax errors with the query validator in BigQuery, and create reports in Looker Studio by connecting to BigQuery data. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
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In this course, you will get hands-on experience applying advanced LookML concepts in Looker. You will learn how to use Liquid to customize and create dynamic dimensions and measures, create dynamic SQL derived tables and customized native derived tables, and use extends to modularize your LookML code.
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In this quest, you will get hands-on experience with LookML in Looker. You will learn how to write LookML code to create new dimensions and measures, create derived tables and join them to Explores, filter Explores, and define caching policies in LookML.
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Complete the introductory Prepare Data for Looker Dashboards and Reports skill badge course to demonstrate skills in the following: filtering, sorting, and pivoting data; merging results from different Looker Explores; and using functions and operators to build Looker dashboards and reports for data analysis and visualization.
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This course empowers you to develop scalable, performant LookML (Looker Modeling Language) models that provide your business users with the standardized, ready-to-use data that they need to answer their questions. Upon completing this course, you will be able to start building and maintaining LookML models to curate and manage data in your organization’s Looker instance.
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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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(Previously named "Developing apps with Vertex AI Agent Builder: Search". Please note there maybe instances in this course where previous product names and titles are used)
Enterprises of all sizes have trouble making their information readily accessible to employees and customers alike. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites often offer a vast selection of products, services, and information, but customers are frustrated by ineffective site search and navigation capabilities.
This course teaches you to use AI Applications to integrate enterprise-grade generative AI search.
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In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. Upon completion of this course, you will be able to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
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In this course, we define what machine learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as build your own Machine Learning models using just SQL with BigQuery ML.
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The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps.
We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views.
After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
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This is the second course in the Data to Insights course series. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Looker Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.
Note: Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you.
After completing this course, enroll in the Achieving Advanced Insights with BigQuery course.
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In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Dataprep to analyze and transform your datasets.
This is the first course of the From Data to Insights with Google Cloud series. After completing this course, enroll in the Creating New BigQuery Datasets and Visualizing Insights course.
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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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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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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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This course will help ML Engineers, Developers, and Data Scientists implement Large Language Models for Generative AI use cases with Vertex AI. The first two modules of this course contain links to videos and prerequisite course materials that will build your knowledge foundation in Generative AI. Please do not skip these modules. The advanced modules in this course assume you have completed these earlier modules.
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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 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 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 teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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This course 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 will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
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Earn a skill badge by completing the Introduction to Generative AI, Introduction to Large Language Models and Introduction to Responsible AI courses. By passing the final quiz, you'll demonstrate your understanding of foundational concepts in generative AI.
A skill badge is a digital badge issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Share your skill badge by making your profile public and adding it to your social media profile.
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This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 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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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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In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
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This course 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 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. 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 skill badge course, and final assessment challenge lab, to receive a digital badge that you can share with your network.
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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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This 1-week, accelerated on-demand course builds upon Google Cloud Platform Big Data and Machine Learning Fundamentals. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn to build streaming data pipelines using Google cloud Pub/Sub and Dataflow to enable real-time decision making. You will also learn how to build dashboards to render tailored output for various stakeholder audiences.
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This course 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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Processing streaming data is becoming increasingly popular as streaming enables businesses to get real-time metrics on business operations. This course covers how to build streaming data pipelines on Google Cloud. Pub/Sub is described for handling incoming streaming data. The course also covers how to apply aggregations and transformations to streaming data using Dataflow, and how to store processed records to BigQuery or Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
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This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.
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This course helps learners create a study plan for the PCA (Professional Cloud Architect) certification exam. Learners explore the breadth and scope of the domains covered in the exam. Learners assess their exam readiness and create their individual study plan.
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Good news! There’s a new updated version of this learning path available for you!Open the new Professional Cloud Architect Certification Learning Path to begin, once you’ve selected the new path all your current progress will be reflected in the new version.
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Earn a skill badge by completing the Set Up a Google Cloud Network skill badge course, where you will learn how to perform basic networking tasks on Google Cloud Platform - create a custom network, add subnets firewall rules, then create VMs and test the latency when they communicate with each other.
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Complete the intermediate Optimize Costs for Google Kubernetes Engine skill badge 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. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge course and the final assessment challenge lab to receive a skill badge that you can share with your network.
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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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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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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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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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This course teaches participants techniques for monitoring and improving infrastructure and application performance in Google Cloud.
Using a combination of presentations, demos, hands-on labs, and real-world case studies, attendees gain experience with full-stack monitoring, real-time log management and analysis, debugging code in production, tracing application performance bottlenecks, and profiling CPU and memory usage.
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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 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. 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, 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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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.
Build intelligent agents with Agent Development Kit (ADK)
Earned Sep 11, 2025 EDT
Model Armor: Securing AI Deployments
Earned Aug 8, 2025 EDT
Generate and Edit Media with Imagen, Gemini, and Veo
Earned Aug 3, 2025 EDT
Extend Gemini with controlled generation and Tool use
Earned Jul 31, 2025 EDT
Empower Gen AI apps with tool use
Earned Jul 31, 2025 EDT
Engineer Effective Prompts for Generative Models
Earned Jul 31, 2025 EDT
Explore Google's Gen AI Models
Earned Jul 31, 2025 EDT
Recommendations with AI Applications
Earned Jul 24, 2025 EDT
Create media search and media recommendations applications with AI Applications
Earned Jul 24, 2025 EDT
Introduction to NotebookLM
Earned Jul 24, 2025 EDT
Configure AI Applications to optimize search results
Earned Jul 24, 2025 EDT
Improve Vertex AI Search and Google Agentspace Search Results
Earned Jul 22, 2025 EDT
Create and maintain Vertex AI Search data stores
Earned Jul 21, 2025 EDT
Improve Performance by Fine-Tuning Foundation Models
Earned Jul 21, 2025 EDT
Create Data Stores for Gen AI Applications
Earned Jul 21, 2025 EDT
Build search and recommendations applications with AI Applications
Earned Jul 21, 2025 EDT
Introduction to AI Applications
Earned Jul 21, 2025 EDT
Vertex AI Search and Google Agentspace Analytics
Earned Jul 10, 2025 EDT
Vertex AI Search and Google Agentspace UI Configurations
Earned Jul 10, 2025 EDT
Gen AI Apps: Transform Your Work
Earned Apr 30, 2025 EDT
Extend Google Agentspace assistant capabilities
Earned Apr 25, 2025 EDT
Deploy Google Agentspace
Earned Apr 25, 2025 EDT
Accelerate Knowledge Exchange with Google Agentspace
Earned Feb 27, 2025 EST
Virtual FAQ with data store agents
Earned Feb 24, 2025 EST
Boost Productivity with Gemini in BigQuery
Earned Feb 21, 2025 EST
Customer Engagement Suite with Google AI Architecture
Earned Feb 11, 2025 EST
Build Generative AI Apps with Firebase Genkit
Earned Jan 19, 2025 EST
Integrate Generative AI Into Your Apps with Firebase Genkit
Earned Jan 12, 2025 EST
Deploy, Test & Evaluate Gen AI Apps
Earned Jan 6, 2025 EST
Orchestrate LLM solutions with LangChain
Earned Jan 2, 2025 EST
Orchestrating Gen AI Applications with LangChain
Earned Dec 23, 2024 EST
Create Generative AI Apps on Google Cloud
Earned Aug 22, 2024 EDT
Inspect Rich Documents with Gemini Multimodality and Multimodal RAG
Earned Aug 14, 2024 EDT
Intro to Conversational AI and Conversational AI Engagement Framework
Earned Aug 2, 2024 EDT
Build deterministic Virtual Agent enhanced with data stores
Earned Aug 2, 2024 EDT
Deploying Search and Chat Apps using Agent Builder
Earned Aug 1, 2024 EDT
Selling the Platform & Building Client Trust
Earned Jul 26, 2024 EDT
Unlocking the Power of Google Cloud Generative AI for Partners
Earned Jul 24, 2024 EDT
Google Cloud Generative AI Trailblazer
Earned Jul 24, 2024 EDT
Improving developer velocity with Gemini Code Assist
Earned Jul 19, 2024 EDT
Monitor and Log with Google Cloud Observability
Earned Jul 15, 2024 EDT
Explore Generative AI with the Gemini API in Vertex AI
Earned Jul 12, 2024 EDT
Build and Deploy a Generative AI solution using a RAG framework
Earned Jul 12, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned Jul 11, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned Jul 5, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned Jul 4, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned Jun 28, 2024 EDT
Vector Search and Embeddings
Earned Jun 24, 2024 EDT
Intro to CCAI and CCAI Engagement Framework
Earned Jun 17, 2024 EDT
Getting Started with MongoDB Atlas on Google Cloud
Earned Jun 17, 2024 EDT
Getting Started with the Vertex AI Gemini API
Earned Jun 15, 2024 EDT
Multimodality with Gemini
Earned Jun 15, 2024 EDT
Develop Gen AI Apps with Gemini and Streamlit
Earned Jun 14, 2024 EDT
Prompt Design in Vertex AI
Earned Jun 3, 2024 EDT
Implement DevOps Workflows in Google Cloud
Earned May 15, 2024 EDT
DEPRECATED Cloud Operations and Service Mesh with Anthos
Earned Apr 10, 2024 EDT
Developing a Google SRE Culture
Earned Apr 3, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned Mar 24, 2024 EDT
Build and Deploy Machine Learning Solutions on Vertex AI
Earned Mar 19, 2024 EDT
ML Pipelines on Google Cloud
Earned Mar 11, 2024 EDT
Machine Learning Operations (MLOps) with Vertex AI: Manage Features
Earned Dec 11, 2023 EST
Machine Learning Operations (MLOps): Getting Started
Earned Dec 9, 2023 EST
Recommendation Systems on Google Cloud
Earned Dec 5, 2023 EST
Natural Language Processing on Google Cloud
Earned Nov 6, 2023 EST
Computer Vision Fundamentals with Google Cloud
Earned Oct 25, 2023 EDT
Generative AI Explorer : Vertex AI
Earned Oct 25, 2023 EDT
Generative AI for Business Leaders
Earned Oct 25, 2023 EDT
Production Machine Learning Systems
Earned Oct 22, 2023 EDT
Machine Learning in the Enterprise
Earned Oct 19, 2023 EDT
Feature Engineering
Earned Oct 16, 2023 EDT
Build, Train and Deploy ML Models with Keras on Google Cloud
Earned Oct 13, 2023 EDT
Introduction to AI and Machine Learning on Google Cloud
Earned Oct 11, 2023 EDT
Data Warehousing for Partners: Analyze Data with Looker
Earned Oct 10, 2023 EDT
Analyzing and Visualizing Data the Google Way
Earned Oct 10, 2023 EDT
Delivery Shadowing
Earned Oct 9, 2023 EDT
Case Studies
Earned Oct 9, 2023 EDT
Technology + Beyond the UI
Earned Oct 9, 2023 EDT
Technology + Within the UI
Earned Oct 8, 2023 EDT
Table Calculations, Pivots, and Visualizations
Earned Oct 8, 2023 EDT
Building Reports in Looker
Earned Oct 8, 2023 EDT
Liquid Templates and Parameters
Earned Oct 6, 2023 EDT
Extends to Keep LookML DRY
Earned Oct 5, 2023 EDT
Admin Roles and Folder Access
Earned Oct 5, 2023 EDT
Version Control and Caching
Earned Oct 5, 2023 EDT
The Modern Data Platform and LookML
Earned Oct 5, 2023 EDT
Driving Data Culture and Designing Dashboards
Earned Oct 3, 2023 EDT
Achieving Business Outcomes with Looker
Earned Oct 2, 2023 EDT
Looker Explained
Earned Oct 2, 2023 EDT
Create ML Models with BigQuery ML
Earned Sep 11, 2023 EDT
Data Catalog Fundamentals
Earned Sep 11, 2023 EDT
Manage Data Models in Looker
Earned Sep 11, 2023 EDT
Build LookML Objects in Looker
Earned Sep 1, 2023 EDT
Derive Insights from BigQuery Data
Earned Aug 29, 2023 EDT
Applying Advanced LookML Concepts in Looker
Earned Aug 28, 2023 EDT
Understanding LookML in Looker
Earned Aug 25, 2023 EDT
Prepare Data for Looker Dashboards and Reports
Earned Aug 7, 2023 EDT
Developing Data Models with LookML
Earned Jul 24, 2023 EDT
Generative AI Fundamentals
Earned Jul 21, 2023 EDT
Search with AI Applications
Earned Jul 19, 2023 EDT
Analyzing and Visualizing Data in Looker
Earned Jul 18, 2023 EDT
Applying Machine Learning to your Data with Google Cloud
Earned Jul 15, 2023 EDT
Achieving Advanced Insights with BigQuery
Earned Jul 14, 2023 EDT
Creating New BigQuery Datasets and Visualizing Insights
Earned Jul 7, 2023 EDT
Exploring and Preparing your Data with BigQuery
Earned Jul 3, 2023 EDT
Launching into Machine Learning
Earned Jul 2, 2023 EDT
Text Prompt Engineering Techniques
Earned Jun 27, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned Jun 26, 2023 EDT
Implementing Generative AI with Vertex AI
Earned Jun 25, 2023 EDT
Generative AI Explorer - Vertex AI
Earned Jun 20, 2023 EDT
Introduction to Vertex AI Studio
Earned Jun 19, 2023 EDT
How Google Does Machine Learning
Earned Jun 19, 2023 EDT
Create Image Captioning Models
Earned Jun 16, 2023 EDT
Transformer Models and BERT Model
Earned Jun 15, 2023 EDT
Encoder-Decoder Architecture
Earned Jun 15, 2023 EDT
Attention Mechanism
Earned Jun 15, 2023 EDT
Introduction to Image Generation
Earned Jun 15, 2023 EDT
Generative AI Fundamentals
Earned Jun 15, 2023 EDT
Introduction to Responsible AI
Earned Jun 15, 2023 EDT
Introduction to Large Language Models
Earned Jun 15, 2023 EDT
Introduction to Generative AI
Earned Jun 15, 2023 EDT
Serverless Data Processing with Dataflow: Develop Pipelines
Earned Jun 13, 2023 EDT
Serverless Data Processing with Dataflow: Operations
Earned Jun 13, 2023 EDT
Preparing for your Professional Data Engineer Journey
Earned Jun 11, 2023 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned Jun 6, 2023 EDT
Build a Data Warehouse with BigQuery
Earned Jun 5, 2023 EDT
Prepare Data for ML APIs on Google Cloud
Earned May 31, 2023 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned May 31, 2023 EDT
Serverless Data Processing with Dataflow: Foundations
Earned Apr 25, 2023 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned Apr 24, 2023 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned Apr 21, 2023 EDT
Building Batch Data Pipelines on Google Cloud
Earned Apr 17, 2023 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Apr 5, 2023 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned Mar 28, 2023 EDT
Preparing for your Professional Cloud Architect Journey
Earned Mar 6, 2023 EST
Certification Learning Path: Professional Cloud Architect
Earned Feb 20, 2023 EST
Set Up a Google Cloud Network
Earned Feb 9, 2023 EST
Optimize Costs for Google Kubernetes Engine
Earned Jan 27, 2023 EST
Reliable Google Cloud Infrastructure: Design and Process
Earned Jan 25, 2023 EST
Build Infrastructure with Terraform on Google Cloud
Earned Jan 18, 2023 EST
Develop Your Google Cloud Network
Earned Jan 17, 2023 EST
Set Up an App Dev Environment on Google Cloud
Earned Jan 16, 2023 EST
Implementing Cloud Load Balancing for Compute Engine
Earned Jan 15, 2023 EST
Getting Started with Terraform for Google Cloud
Earned Jan 15, 2023 EST
Logging and Monitoring in Google Cloud
Earned Jan 13, 2023 EST
Getting Started with Google Kubernetes Engine
Earned Jan 10, 2023 EST
Essential Google Cloud Infrastructure: Core Services
Earned Jan 9, 2023 EST
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned Jan 8, 2023 EST
Essential Google Cloud Infrastructure: Foundation
Earned Jan 5, 2023 EST
Google Cloud Fundamentals: Core Infrastructure
Earned Jan 4, 2023 EST
Preparing for Your Associate Cloud Engineer Journey
Earned Jan 3, 2023 EST
Scaling with Google Cloud Operations
Earned Dec 29, 2022 EST
Modernize Infrastructure and Applications with Google Cloud
Earned Dec 29, 2022 EST
Exploring Data Transformation with Google Cloud
Earned Dec 28, 2022 EST
Digital Transformation with Google Cloud
Earned Dec 27, 2022 EST