Customer Engagement Suite with Google AI Architecture
Earned сент. 3, 2024 EDT
Webhook fundamentals
Earned авг. 11, 2024 EDT
Intro to Conversational AI and Conversational AI Engagement Framework
Earned авг. 11, 2024 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned авг. 11, 2024 EDT
Detect Manufacturing Defects Using Visual Inspection AI
Earned авг. 6, 2024 EDT
Serverless Data Processing with Dataflow: Operations
Earned авг. 5, 2024 EDT
Vector Search and Embeddings
Earned июля 31, 2024 EDT
Observability in Google Cloud
Earned июля 30, 2024 EDT
Prepare Data for Looker Dashboards and Reports
Earned июля 23, 2024 EDT
Implementing Generative AI with Vertex AI
Earned июля 21, 2024 EDT
Create Image Captioning Models
Earned июля 15, 2024 EDT
Transformer Models and BERT Model
Earned июля 14, 2024 EDT
Encoder-Decoder Architecture
Earned июля 13, 2024 EDT
Attention Mechanism
Earned июля 13, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned июля 12, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned июля 4, 2024 EDT
Prompt Design in Vertex AI
Earned июля 3, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned июля 1, 2024 EDT
Generative AI Fundamentals
Earned июня 25, 2024 EDT
Build and Deploy Machine Learning Solutions on Vertex AI
Earned мая 24, 2024 EDT
Machine Learning in the Enterprise
Earned мая 22, 2024 EDT
Text Prompt Engineering Techniques
Earned мая 15, 2024 EDT
How Google Does Machine Learning
Earned мая 14, 2024 EDT
Introduction to Image Generation
Earned мая 14, 2024 EDT
Generative AI Explorer : Vertex AI
Earned мая 12, 2024 EDT
Generative AI Fundamentals
Earned мая 11, 2024 EDT
Introduction to Vertex AI Studio
Earned мая 11, 2024 EDT
Generative AI for Business Leaders
Earned мая 11, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned апр. 29, 2024 EDT
Build a Data Warehouse with BigQuery
Earned апр. 27, 2024 EDT
Introduction to Responsible AI
Earned апр. 22, 2024 EDT
Introduction to Large Language Models
Earned апр. 22, 2024 EDT
Introduction to Generative AI
Earned апр. 21, 2024 EDT
Build a Data Mesh with Dataplex
Earned апр. 4, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned апр. 3, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned марта 21, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned марта 20, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned марта 17, 2024 EDT
Building Batch Data Pipelines on Google Cloud
Earned февр. 27, 2024 EST
Develop Your Google Cloud Network
Earned февр. 26, 2024 EST
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned февр. 21, 2024 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned февр. 12, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned февр. 10, 2024 EST
Build Infrastructure with Terraform on Google Cloud
Earned февр. 8, 2024 EST
Set Up an App Dev Environment on Google Cloud
Earned февр. 5, 2024 EST
Implementing Cloud Load Balancing for Compute Engine
Earned февр. 4, 2024 EST
Getting Started with Terraform for Google Cloud
Earned февр. 4, 2024 EST
Logging and Monitoring in Google Cloud
Earned февр. 4, 2024 EST
Getting Started with Google Kubernetes Engine
Earned янв. 31, 2024 EST
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned янв. 30, 2024 EST
Essential Google Cloud Infrastructure: Core Services
Earned янв. 27, 2024 EST
Essential Google Cloud Infrastructure: Foundation
Earned янв. 23, 2024 EST
Google Cloud Fundamentals: Core Infrastructure
Earned янв. 14, 2024 EST
Preparing for Your Associate Cloud Engineer Journey - Locales
Earned дек. 18, 2023 EST
Preparing for Your Associate Cloud Engineer Journey
Earned дек. 11, 2023 EST
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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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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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 the quality assurance best practices and the tools available in Conversational Agents to ensure production grade quality during Conversational Agent development, as well as the key tenets for the creation of a robust end to end deployment lifecycle. 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 intermediate Implement Cloud Security Fundamentals on Google Cloud skill badge course to demonstrate skills in the following: creating and assigning roles with Identity and Access Management (IAM); creating and managing service accounts; enabling private connectivity across virtual private cloud (VPC) networks; restricting application access using Identity-Aware Proxy; managing keys and encrypted data using Cloud Key Management Service (KMS); and creating a private Kubernetes cluster.
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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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If you are a novice cloud developer looking for hands-on practice beyond Google Cloud Essentials, this course is for you. You will get practical experience through labs that dive into Cloud Storage and other key application services like Monitoring and Cloud Functions. You will develop valuable skills that are applicable to any Google Cloud initiative. 1-minute videos walk you through key concepts for these labs.
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Complete the introductory Derive Insights from BigQuery Data skill badge course 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.Create reports in Looker Studio by connecting to BigQuery data.
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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 explores the fundamentals of the feedback loop process for Conversational Agent development and introduces the native capabilities within Conversational Agents that support it. You will also learn about advanced methods and tools to monitor the performance of your Conversational agent in Conversational Agents.
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Discover flows in Conversational Agents and learn how to build deterministic chat and voice experiences with language models. Explore key concepts like drivers, intents, and entities, and how to use them to create conversational agents.
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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 explores the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel. 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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This course explores the best practices, methods and tools to programmatically lead CCAI virtual agent delivery. It includes a high level overview of the end to end journey for building and deploying a virtual agent, as well as the core tenets to create a strong delivery culture. Additionally, this course covers the best practices for workflow management, defect tracking, release management and post-release support to ensure optimal virtual agent performance.
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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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In this course, you will learn the important role that different types of webhooks play in Conversational Agents development, and how to effectively integrate them into your routine configuration of a Conversational Agent. 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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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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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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Earn a skill badge by completing the Detect Manufacturing Defects using Visual Inspection AI course, where you learn how to use Visual Inspection AI to deploy a solution artifact and test that it can successfully identify defects in a manufacturing process.
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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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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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Welcome to the second part of the two part course, Observability in Google Cloud.
This course is all about application performance management tools, including Error Reporting, Cloud Trace, and Cloud Profiler.
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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 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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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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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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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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Complete the introductory Prompt Design in Vertex AI skill badge to demonstrate skills in the following: prompt engineering, image analysis, and multimodal generative techniques, within Vertex AI. Discover how to craft effective prompts, guide generative AI output, and apply Gemini models to real-world marketing scenarios.
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This course 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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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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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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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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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 explores what ML is and what problems it can solve. The course also discusses best practices for implementing machine learning. You’re introduced to Vertex AI, a unified platform to quickly build, train, and deploy AutoML machine learning models.
The course discusses the five phases of converting a candidate use case to be driven by machine learning, and why it’s important to not skip them. The course ends with recognizing the biases that ML can amplify and how to recognize them.
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This course introduces 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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This content is deprecated. Please see the latest version of the course, here.
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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 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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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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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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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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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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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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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 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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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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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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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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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 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 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 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. 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, Preparing for Your Associate Cloud Engineer Journey - Locales, is intended for non-English learners. If you want to take this course in English, please enroll in Preparing for Your Associate Cloud Engineer Journey.
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 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.
Work with Gemini Models in BigQuery
Earned янв. 20, 2025 EST
Build deterministic Virtual Agent enhanced with data stores
Earned янв. 18, 2025 EST
Virtual FAQ with data store agents
Earned янв. 18, 2025 EST
Conversational Agents Quality Assurance and Deployment Lifecycle
Earned янв. 18, 2025 EST
Implement Cloud Security Fundamentals on Google Cloud
Earned дек. 2, 2024 EST
Introduction to AI and Machine Learning on Google Cloud
Earned нояб. 19, 2024 EST
Baseline: Infrastructure
Earned нояб. 18, 2024 EST
Derive Insights from BigQuery Data
Earned нояб. 18, 2024 EST
Introduction to Data Engineering on Google Cloud
Earned нояб. 15, 2024 EST
Performance Measurement
Earned нояб. 4, 2024 EST
Stateful Flows
Earned нояб. 3, 2024 EST
Custom Search with Embeddings in Vertex AI
Earned нояб. 1, 2024 EDT
Conversation Design Fundamentals
Earned нояб. 1, 2024 EDT
DFCX Virtual Agent Delivery Framework
Earned нояб. 1, 2024 EDT
Customer Engagement Suite with Google AI Architecture
Earned сент. 3, 2024 EDT
Webhook fundamentals
Earned авг. 11, 2024 EDT
Intro to Conversational AI and Conversational AI Engagement Framework
Earned авг. 11, 2024 EDT
Building Resilient Streaming Analytics Systems on Google Cloud
Earned авг. 11, 2024 EDT
Detect Manufacturing Defects Using Visual Inspection AI
Earned авг. 6, 2024 EDT
Serverless Data Processing with Dataflow: Operations
Earned авг. 5, 2024 EDT
Vector Search and Embeddings
Earned июля 31, 2024 EDT
Observability in Google Cloud
Earned июля 30, 2024 EDT
Prepare Data for Looker Dashboards and Reports
Earned июля 23, 2024 EDT
Implementing Generative AI with Vertex AI
Earned июля 21, 2024 EDT
Create Image Captioning Models
Earned июля 15, 2024 EDT
Transformer Models and BERT Model
Earned июля 14, 2024 EDT
Encoder-Decoder Architecture
Earned июля 13, 2024 EDT
Attention Mechanism
Earned июля 13, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned июля 12, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned июля 4, 2024 EDT
Prompt Design in Vertex AI
Earned июля 3, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned июля 1, 2024 EDT
Generative AI Fundamentals
Earned июня 25, 2024 EDT
Build and Deploy Machine Learning Solutions on Vertex AI
Earned мая 24, 2024 EDT
Machine Learning in the Enterprise
Earned мая 22, 2024 EDT
Text Prompt Engineering Techniques
Earned мая 15, 2024 EDT
How Google Does Machine Learning
Earned мая 14, 2024 EDT
Introduction to Image Generation
Earned мая 14, 2024 EDT
Generative AI Explorer : Vertex AI
Earned мая 12, 2024 EDT
Generative AI Fundamentals
Earned мая 11, 2024 EDT
Introduction to Vertex AI Studio
Earned мая 11, 2024 EDT
Generative AI for Business Leaders
Earned мая 11, 2024 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned апр. 29, 2024 EDT
Build a Data Warehouse with BigQuery
Earned апр. 27, 2024 EDT
Introduction to Responsible AI
Earned апр. 22, 2024 EDT
Introduction to Large Language Models
Earned апр. 22, 2024 EDT
Introduction to Generative AI
Earned апр. 21, 2024 EDT
Build a Data Mesh with Dataplex
Earned апр. 4, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned апр. 3, 2024 EDT
Serverless Data Processing with Dataflow: Foundations
Earned марта 21, 2024 EDT
Smart Analytics, Machine Learning, and AI on Google Cloud
Earned марта 20, 2024 EDT
Building Resilient Streaming Systems on Google Cloud Platform
Earned марта 17, 2024 EDT
Building Batch Data Pipelines on Google Cloud
Earned февр. 27, 2024 EST
Develop Your Google Cloud Network
Earned февр. 26, 2024 EST
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned февр. 21, 2024 EST
Google Cloud Big Data and Machine Learning Fundamentals
Earned февр. 12, 2024 EST
Preparing for your Professional Data Engineer Journey
Earned февр. 10, 2024 EST
Build Infrastructure with Terraform on Google Cloud
Earned февр. 8, 2024 EST
Set Up an App Dev Environment on Google Cloud
Earned февр. 5, 2024 EST
Implementing Cloud Load Balancing for Compute Engine
Earned февр. 4, 2024 EST
Getting Started with Terraform for Google Cloud
Earned февр. 4, 2024 EST
Logging and Monitoring in Google Cloud
Earned февр. 4, 2024 EST
Getting Started with Google Kubernetes Engine
Earned янв. 31, 2024 EST
Elastic Google Cloud Infrastructure: Scaling and Automation
Earned янв. 30, 2024 EST
Essential Google Cloud Infrastructure: Core Services
Earned янв. 27, 2024 EST
Essential Google Cloud Infrastructure: Foundation
Earned янв. 23, 2024 EST
Google Cloud Fundamentals: Core Infrastructure
Earned янв. 14, 2024 EST
Preparing for Your Associate Cloud Engineer Journey - Locales
Earned дек. 18, 2023 EST
Preparing for Your Associate Cloud Engineer Journey
Earned дек. 11, 2023 EST