Google Cloud

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. You learn to leverage Looker's modern analytics platform to find and explore relevant content in your organization’s Looker instance. You also discover how to ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision-making.

This course introduces you to the fundamentals, practices, capabilities and tools for modern cloud application development with Cloud Run. Through a combination of lectures, hands-on labs, and supplemental materials, you learn how to develop and deploy applications on Google Cloud by using Cloud Run. This version of the course consists of two separate courses that include topics on creating containers and developing applications with Cloud Run. The courses are: Developing Containerized Applications on Google Cloud, Developing Applications with Cloud Run on Google Cloud: Fundamentals.

In this course, you explore tools and APIs available on Google Cloud for integrating large language models (LLMs) into your application. After exploring generative AI options on Google Cloud, you explore LLMs and prompt design in Vertex AI Studio. Then you learn about LangChain, an open-source framework for developing applications powered by language models. After a discussion around more advanced prompt engineering techniques, you put it all together to build a multi-turn chat application by using LangChain and the Vertex AI Gemini API.

This course features a combination of lectures, design activities, and hands-on labs to show you how to use proven design patterns on Google Cloud to build highly reliable and efficient solutions and operate deployments that are highly available and cost-effective. This course was created for those who have already completed the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine course.

In this course, you will become familiar with the flexible infrastructure and platform services of Google Cloud, with a specific focus on Compute Engine.

This training session uses a combination of lessons, demos, and hands-on labs to implement and explore solution elements, including infrastructure components such as networks, systems, and application services.

You will also learn how to implement practical solutions such as hybrid networks, customer-supplied encryption keys, security and access management, quotas, billing, and resource monitoring.

Learn how to deploy and manage containerized applications on Google Kubernetes Engine (GKE). Learn how to use other tools on Google Cloud that interact with GKE deployments.

This course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements—including infrastructure components like pods, containers, deployments, and services—along with networks and application services. You‘ll also learn how to deploy practical solutions, including security and access management, resource management and resource monitoring.

If you’re wondering how the cloud can transform your business, then the Cloud Digital Leader training is for you. Designed to give you foundational knowledge about cloud technology, data, artificial intelligence, and Google Cloud products that enable digital transformation, CDL can empower you and your team(s) to contribute to cloud-related business initiatives in your organization.

Whether you're an experienced leader or new to the cloud, CDL equips you with the tools and confidence to make a real difference.

Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hands-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data.

This 2-day course introduces learners to Google Cloud’s data integration capability using Cloud Data Fusion.

In this course, we discuss challenges with data integration and the need for a data integration platform (middleware).

We then discuss how Cloud Data Fusion can help to effectively integrate data from a variety of sources and formats and generate insights.

We take a look at Cloud Data Fusion’s main components and how they work, how to process batch data and real time streaming data with visual pipeline design, rich tracking of metadata and data lineage, and how to deploy data pipelines on various execution engines.

In this course, you learn about Cloud Run functions, Google's serverless, fullymanaged functions-as-a-service (FaaS) product. With Cloud Run functions, you implement single-purpose functions that respond to HTTP requests and process events from your cloud infrastructure.

In this instructor-led course, you learn the fundamentals of application development on Google Cloud. You learn best practices for designing cloud applications, and how to choose compute and data options for your apps. You learn about authentication and authorization, generative AI for developers, and continuous integration and delivery for your apps running in Google Cloud. You learn about event-based applications and the use of service orchestration and choreography to coordinate microservices. You learn about Cloud Functions, which allows you to implement single-purpose functions that respond to HTTP requests and process events within your cloud infrastructure.

This course empowers you to develop scalable, performant Looker Modeling Language (LookML) 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.

This course is designed for business users, business leaders, and decision makers who want to understand the transformative potential of generative AI and its impact on their organizations.

You'll gain a comprehensive understanding of this technology, learn how it can be leveraged to drive innovation and efciency, and explore the range of generative AI services available on Google Cloud. By the end of this course, you'll be equipped to make informed decisions about implementing AI solutions.

Explore ways to derive insights from data at scale using BigQuery, Google Cloud’s serverless, highly scalable, and cost-effective cloud data warehouse. This course uses lectures, demos, and hands-on labs to teach you the fundamentals of BigQuery, including how to create a data transformation pipeline, build a BI dashboard, ingest new datasets, and design schemas at scale.

This course covers an introduction to Kubernetes, a software layer that sits between your applications and your hardware infrastructure. Google Kubernetes Engine (GKE) brings you Kubernetes as a managed service on Google Cloud. This course teaches the basics of GKE and how to get applications containerized and running in Google Cloud. The course covers a basic introduction to Google Cloud, an overview of containers and Kubernetes, Kubernetes architecture and Kubernetes operations.

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 a 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 Google Cloud resources using Terraform.

This course uses lectures and labs to give you an overview of Google Cloud products and services. You learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies.

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 is an introduction to data analytics on Google Cloud. It is designed for learners who have no prior experience with data analytics or Google Cloud.

The course covers the basics of data analysis, including collection, storage, exploration, visualization, and sharing. It also introduces learners to Google Cloud's data analytics tools and services. Through lectures, demos, quizzes, and hands-on labs, the course demonstrates how to go from raw data to impactul visualizations and dashboards.

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.

Generative AI is now at the center of transforming how software is designed, built, run, and managed.

For developers, generative AI is a powerful tool for making coding more efficient and using APIs, such as the Gemini and PaLM APIs, within their applications.

In this course, you are introduced to how generative AI can be used to make developers more efficient at writing code and implementing new features into applications. You will also explore available models in Vertex AI Model Garden.

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.

LookML not only serves as the foundation for visualization assets in Looker, but is also capable of dynamic aggregations, incrementally refreshed persistent derived tables, and more. In this course you will practice the skills to be an advanced Looker Developer through guided lectures and independent exercises using sample data.

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques and feature engineering.

This training course builds on the networking concepts covered in the Networking Fundamentals in Google Cloud course. Through presentations, demonstrations, and labs, participants explore and deploy Google Cloud networking technologies. These technologies include: Virtual Private Cloud (VPC) networks, subnets, and firewalls; Interconnection among networks; Load balancing ;Cloud DNS; Cloud CDN; Cloud NAT. The course will also cover common network design patterns.

This training course gives you a broad study of security controls and techniques in Google Cloud. Through lectures, demonstrations, and labs, you explore and deploy the components of a secure Google Cloud solution. You use services including Cloud Identity, Identity and Access Management (IAM), Cloud Load Balancing, Cloud IDS, Web Security Scanner, BeyondCorp Enterprise, and Cloud DNS.

This training is intended for big data practitioners who want to further their understanding of Dataflow in order to advance their data processing applications.

Beginning with foundations, this training explains how Apache Beam and Dataflow work together to meet your data processing needs without the risk of vendor lock-in. The section on developing pipelines covers how you convert your business logic into data processing applications that can run on Dataflow.

This training culminates with a focus on operations, which reviews the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability.

Vertex AI Agent Builder lets developers, even those with limited machine learning skills, tap into the power of Google's foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications.

In this course, you learn how to use Vertex AI Agent Builder to create search engines and chat applications. You will then explore how to integrate these search engineers and chat applications into your own applications. Finally, you learn how to manage the tools built in Vertex AI Agent Builder in production.

This instructor-led one-day course is designed for engineers and data scientists familiar with machine learning models who want to become proficient in using Vertex AI for custom model workflows. This practical, hands-on course will provide you with a deep dive into the core functionalities of Vertex AI, enabling you to effectively leverage its tools and capabilities for your ML projects.

This course is an introduction to building forecasting solutions with Google Cloud. You start with sequence models and time series foundations. You then walk through an end-to-end workflow: from data preparation to model development and deployment with Vertex AI. Finally, you learn the lessons and tips from a retail use case and apply the knowledge by building your own forecasting models.