Maria Lopez
Учасник із 2023
Діамантова ліга
Кількість балів: 46505
Учасник із 2023
This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.
Пройдіть вступний кваліфікаційний курс Підготовка даних для інтерфейсів API машинного навчання в Google Cloud, щоб продемонструвати свої навички щодо очистки даних за допомогою сервісу Dataprep by Trifacta, запуску конвеєрів даних у Dataflow, створення кластерів і запуску завдань Apache Spark у Dataproc, а також виклику API машинного навчання, зокрема Cloud Natural Language API, Google Cloud Speech-to-Text API і Video Intelligence API. Кваліфікаційний значок – це ексклюзивна цифрова відзнака, яка підтверджує, що ви вмієте працювати з продуктами й сервісами Google Cloud і можете застосовувати ці знання в інтерактивному практичному середовищі. Щоб отримати кваліфікаційний значок і показати його колегам, пройдіть цей курс і підсумковий тест.
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.
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.
This course helps learners create a study plan for the PMLE (Professional Machine Learning 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.
Gen AI Agents: Transform Your Organization is the fifth and final course of the Gen AI Leader learning path. This course explores how organizations can use custom gen AI agents to help tackle specific business challenges. You gain hands-on practice building a basic gen AI agent, while exploring the components of these agents, such as models, reasoning loops, and tools.
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.
Gen AI: Navigate the Landscape is the third course of the Gen AI Leader learning path. Gen AI is changing how we work and interact with the world around us. But as a leader, how can you harness its power to drive real business outcomes? In this course, you explore the different layers of building gen AI solutions, Google Cloud’s offerings, and the factors to consider when selecting a solution.
Gen AI: Unlock Foundational Concepts is the second course of the Gen AI Leader learning path. In this course, you unlock the foundational concepts of generative AI by exploring the differences between AI, ML, and gen AI, and understanding how various data types enable generative AI to address business challenges. You also gain insights into Google Cloud strategies to address the limitations of foundation models and the key challenges for responsible and secure AI development and deployment.
Gen AI: Beyond the Chatbot is the first course of the Gen AI Leader learning path and has no prerequisites. This course aims to move beyond the basic understanding of chatbots to explore the true potential of generative AI for your organization. You explore concepts like foundation models and prompt engineering, which are crucial for leveraging the power of gen AI. The course also guides you through important considerations you should make when developing a successful gen AI strategy for your organization.
Що більше штучний інтелект і машинне навчання використовуються в корпоративних середовищах, то нагальнішою стає потреба розробити принципи відповідального ставлення до них. Однак говорити про принципи відповідального використання штучного інтелекту легше, ніж застосовувати їх на практиці. Цей курс допоможе вам дізнатись, як запровадити відповідальну роботу зі штучним інтелектом у вашій організації. У цьому курсі ви дізнаєтеся про підхід Google Cloud до відповідального використання ШІ, а також отримаєте практичні поради й набудете досвіду, який допоможе вам розробити власний підхід до цього завдання.
Це ознайомлювальний курс мікронавчання, який має пояснити, що таке відповідальне використання штучного інтелекту, чому воно важливе і як компанія Google реалізує його у своїх продуктах. Крім того, у цьому курсі викладено 7 принципів Google щодо штучного інтелекту.
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.
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.
In this course, you shadow a series of client meetings led by a Looker Professional Services Consultant.
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.
In this course you will discover additional tools for your toolbox for working with complex deployments, building robust solutions, and delivering even more value.
Develop technical skills beyond LookML along with basic administration for optimizing Looker instances
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.
This course reviews the processes for creating table calculations, pivots and visualizations
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)
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.
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.
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.
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
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.
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.
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.
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.
While the traditional approaches of using data lakes and data warehouses can be effective, they have shortcomings, particularly in large enterprise environments. This course introduces the concept of a data lakehouse and the Google Cloud products used to create one. A lakehouse architecture uses open-standard data sources and combines the best features of data lakes and data warehouses, which addresses many of their shortcomings.
Під час курсу ви зможете ознайомитися з продуктами й сервісами Google Cloud для роботи з масивами даних і машинним навчанням, які підтримують життєвий цикл роботи з даними для тренування моделей штучного інтелекту. У курсі розглядаються процеси, проблеми й переваги створення конвеєру масиву даних і моделей машинного навчання з Vertex AI у Google Cloud.
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.
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.
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.
Курс "Знайомство з Google Cloud: основна інфраструктура" охоплює важливі поняття й терміни щодо використання Google Cloud. Переглядаючи відео й виконуючи практичні завдання, слухачі ознайомляться з різними сервісами Google Cloud для обчислень і зберігання даних, а також важливими ресурсами й інструментами для керування правилами. Крім того, вони зможуть їх порівнювати.