Use Machine Learning APIs on Google Cloud
Earned avr. 11, 2024 EDT
How Google Does Machine Learning
Earned avr. 10, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned avr. 10, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned avr. 8, 2024 EDT
Orchestrate LLM solutions with LangChain
Earned mars 26, 2024 EDT
Generative AI Fundamentals
Earned mars 23, 2024 EDT
Text Prompt Engineering Techniques
Earned mars 21, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned mars 21, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned mars 21, 2024 EDT
Vector Search and Embeddings
Earned mars 11, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned nov. 23, 2023 EST
What's New from Next '23
Earned nov. 22, 2023 EST
Introduction to Gemini Enterprise for Customer Experience and Conversational Agents
Earned nov. 22, 2023 EST
Implementing Generative AI with Vertex AI
Earned sept. 21, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned sept. 21, 2023 EDT
Generative AI Fundamentals
Earned juin 7, 2023 EDT
Generative AI Explorer - Vertex AI
Earned juin 7, 2023 EDT
Introduction to Vertex AI Studio
Earned juin 6, 2023 EDT
Create Image Captioning Models
Earned juin 6, 2023 EDT
Transformer Models and BERT Model
Earned juin 6, 2023 EDT
Attention Mechanism
Earned juin 6, 2023 EDT
Encoder-Decoder Architecture
Earned juin 5, 2023 EDT
Introduction to Image Generation
Earned juin 5, 2023 EDT
Introduction to Responsible AI
Earned juin 5, 2023 EDT
Introduction to Large Language Models
Earned juin 5, 2023 EDT
Introduction to Generative AI
Earned juin 5, 2023 EDT
Getting Started with Google Kubernetes Engine
Earned avr. 19, 2022 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation - Locales
Earned avr. 11, 2022 EDT
Essential Google Cloud Infrastructure: Core Services
Earned mars 31, 2022 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned mars 29, 2022 EDT
Essential Google Cloud Infrastructure: Foundation
Earned mars 21, 2022 EDT
Unite Google’s expertise in search and AI with Gemini Enterprise, a powerful tool designed to help employees find specific information from document storage, email, chats, ticketing systems, and other data sources, all from a single search bar. The Gemini Enterprise assistant can also help brainstorm, research, outline documents, and take actions like inviting coworkers to a calendar event to accelerate knowledge work and collaboration of all kinds.
(Please note Gemini Enterprise was previously named Google Agentspace, there may be references to the previous product name in this course.)
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This course will focus on Agent Assist, an AI-powered tool designed to enhance customer service interactions. In this course, you will learn how Agent Assist can enhance the productivity of human agents while interacting with customers through the chat channel. You’ll learn how to take full advantage of Agent Assist from Gemini Enterprise for Customer Experience, and its range of Gen AI features and functionality.
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In this course, you'll learn to develop AI agents that answer questions using websites, documents, or structured data. You will explore 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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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.
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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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Configure and Maintain CCaaS as an Admin is a course that provides end users with essential learning about the core features, functionality, reporting, and configuration information most relevant to the role. This course is most appropriate for those who perform administrative functions to support the operation of the contact center as well as analyze, troubleshoot, and configure the platform to best meet the demands of customers. Although this program will review some monitoring and reporting aspects, those topics are explored in depth in the course titled "Managing Functions and Reporting with CCaSS."
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Manage Functions and Reporting with CCaaS provides end-users with essential training about the core features, functionality, monitoring, reporting, and configuration information that is most relevant to the role. This course is most appropriate for those at the managerial level of the contact center who are tasked with monitoring the effectiveness, efficiency, and KPI attainment for all consumer interactions. While this program will review some aspects of settings and configuration options, the major focus is on reporting functionality in CCaaS.
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This course teaches contact center agents about the core agent features and functionality in Contact Center as a Service (CCaaS). CCaaS is a unified contact center platform that accelerates an organization's ability to leverage and deploy contact centers without relying on multiple technology providers. This course is most appropriate for those who handle consumer interactions via chat and call.
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An AI-driven Contact Center as a Service (CCaaS) solution that is built natively on Google Cloud. The Implementation course provides Partners with essential training about the delivery of key features and functionality. The course explores how to leverage your key understanding of the product into successful customer implementation engagements with tips, best practices, guides, and more.
Note: This product was previously called Contact Center AI (CCAI) Platform you may see references to that name still in the course, however the course is technically correct.
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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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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 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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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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Complete the intermediate Create ML Models with BigQuery ML skill badge to demonstrate skills in creating and evaluating machine learning models with BigQuery ML to make data predictions.
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This course 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 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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Earn the advanced skill badge by completing the Use Machine Learning APIs on Google Cloud course, where you learn the basic features for the following machine learning and AI technologies: Cloud Vision API, Cloud Translation API, and Cloud Natural Language API.
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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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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 intermediate course, you will learn to design, build, and optimize robust batch data pipelines on Google Cloud. Moving beyond fundamental data handling, you will explore large-scale data transformations and efficient workflow orchestration, essential for timely business intelligence and critical reporting.
Get hands-on practice using Dataflow for Apache Beam and Serverless for Apache Spark (Dataproc Serverless) for implementation, and tackle crucial considerations for data quality, monitoring, and alerting to ensure pipeline reliability and operational excellence. A basic knowledge of data warehousing, ETL/ELT, SQL, Python, and Google Cloud concepts is recommended.
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Learn to use LangChain to call Google Cloud LLMs and Generative AI Services and Datastores to simplify complex applications' code.
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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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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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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 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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Explore AI-powered search technologies, tools, and applications in this course. Learn semantic search utilizing vector embeddings, hybrid search combining semantic and keyword approaches, and retrieval-augmented generation (RAG) minimizing AI hallucinations as a grounded AI agent. Gain practical experience with Vertex AI Vector Search to build your intelligent search engine.
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(This course 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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This short course we'll get you up to speed on all the Vertex AI related announcements made at Google Cloud Next '23!
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This course explores the different products and capabilities of Gemini Enterprise for Customer Experience and Conversational Agents. Additionally, it covers the foundational principles of conversation design to craft engaging and effective experiences that emulate human-like experiences specific to the Chat channel.
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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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As the use of enterprise Artificial Intelligence and Machine Learning continues to grow, so too does the importance of building it responsibly. A challenge for many is that talking about responsible AI can be easier than putting it into practice. If you’re interested in learning how to operationalize responsible AI in your organization, this course is for you.
In this course, you will learn how Google Cloud does this today, together with best practices and lessons learned, to serve as a framework for you to build your own responsible AI approach.
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Earn a skill badge by 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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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 teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images
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This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.This course is estimated to take approximately 45 minutes to complete.
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This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. This course is estimated to take approximately 45 minutes to complete.
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This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.
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This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
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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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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 course version is for non-English only. If you wish to take this course in English, please enroll here: Elastic Google Cloud Infrastructure: Scaling and Automation. If you wish to take it in another language, change your language in settings to see availability.
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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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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 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.
Accelerate Knowledge Exchange with Gemini Enterprise
Earned fév. 10, 2025 EST
Introduction to Agent Assist and its GenAI Capabilities
Earned jan. 10, 2025 EST
Extend CX Agents with Vertex AI Search data stores
Earned déc. 6, 2024 EST
Customer Experience with Google AI Architecture
Earned déc. 4, 2024 EST
Intro to Conversational AI and Conversational AI Engagement Framework
Earned déc. 3, 2024 EST
Configure and Maintain CCaaS as an Admin
Earned nov. 27, 2024 EST
Manage Functions and Reporting with CCaaS
Earned nov. 19, 2024 EST
Handle Consumer Interactions with CCaaS
Earned nov. 14, 2024 EST
Contact Center as a Service Implementation
Earned nov. 14, 2024 EST
Build and Deploy a Generative AI solution using a RAG framework
Earned août 28, 2024 EDT
Integrate Vertex AI Search and Conversation into Voice and Chat Apps
Earned juil. 11, 2024 EDT
DEPRECATED Build and Deploy Machine Learning Solutions on Vertex AI
Earned avr. 19, 2024 EDT
Prepare Data for ML APIs on Google Cloud
Earned avr. 19, 2024 EDT
Create ML Models with BigQuery ML
Earned avr. 18, 2024 EDT
Google Cloud Big Data and Machine Learning Fundamentals
Earned avr. 18, 2024 EDT
Machine Learning Operations (MLOps): Getting Started
Earned avr. 11, 2024 EDT
Use Machine Learning APIs on Google Cloud
Earned avr. 11, 2024 EDT
How Google Does Machine Learning
Earned avr. 10, 2024 EDT
Engineer Data for Predictive Modeling with BigQuery ML
Earned avr. 10, 2024 EDT
Build Batch Data Pipelines on Google Cloud
Earned avr. 8, 2024 EDT
Orchestrate LLM solutions with LangChain
Earned mars 26, 2024 EDT
Generative AI Fundamentals
Earned mars 23, 2024 EDT
Text Prompt Engineering Techniques
Earned mars 21, 2024 EDT
Develop Advanced Enterprise Search and Conversation Applications
Earned mars 21, 2024 EDT
Custom Search with Embeddings in Vertex AI
Earned mars 21, 2024 EDT
Vector Search and Embeddings
Earned mars 11, 2024 EDT
Building Gen AI Apps with Vertex AI: Prompting and Tuning
Earned nov. 23, 2023 EST
What's New from Next '23
Earned nov. 22, 2023 EST
Introduction to Gemini Enterprise for Customer Experience and Conversational Agents
Earned nov. 22, 2023 EST
Implementing Generative AI with Vertex AI
Earned sept. 21, 2023 EDT
Responsible AI: Applying AI Principles with Google Cloud
Earned sept. 21, 2023 EDT
Generative AI Fundamentals
Earned juin 7, 2023 EDT
Generative AI Explorer - Vertex AI
Earned juin 7, 2023 EDT
Introduction to Vertex AI Studio
Earned juin 6, 2023 EDT
Create Image Captioning Models
Earned juin 6, 2023 EDT
Transformer Models and BERT Model
Earned juin 6, 2023 EDT
Attention Mechanism
Earned juin 6, 2023 EDT
Encoder-Decoder Architecture
Earned juin 5, 2023 EDT
Introduction to Image Generation
Earned juin 5, 2023 EDT
Introduction to Responsible AI
Earned juin 5, 2023 EDT
Introduction to Large Language Models
Earned juin 5, 2023 EDT
Introduction to Generative AI
Earned juin 5, 2023 EDT
Getting Started with Google Kubernetes Engine
Earned avr. 19, 2022 EDT
Elastic Google Cloud Infrastructure: Scaling and Automation - Locales
Earned avr. 11, 2022 EDT
Essential Google Cloud Infrastructure: Core Services
Earned mars 31, 2022 EDT
Google Cloud Fundamentals: Core Infrastructure
Earned mars 29, 2022 EDT
Essential Google Cloud Infrastructure: Foundation
Earned mars 21, 2022 EDT