Google Cloud Courses

This course uses lectures and labs to give you an overview of Google Cloud products and services. You learn about the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies.
Google Cloud Fundamentals: Core Infrastructure (GCF-CI) Objectives
Identify the purpose and value of Google Cloud products and services.
Define how infrastructure is organized and controlled in Google Cloud.
Explain how to create basic infrastructure in Google Cloud.
Select and use Google Cloud storage options.
Describe the purpose and value of Google Kubernetes Engine.
Identify the use cases for serverless Google Cloud services.
Explore Google Clouds Generative AI tools and best practices.

This course uses lectures and labs to give you an overview of Google Cloud products and services. You learn about the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies.
Google Cloud Fundamentals: Core Infrastructure (GCF-CI) Objectives
Identify the purpose and value of Google Cloud products and services.
Define how infrastructure is organized and controlled in Google Cloud.
Explain how to create basic infrastructure in Google Cloud.
Select and use Google Cloud storage options.
Describe the purpose and value of Google Kubernetes Engine.
Identify the use cases for serverless Google Cloud services.
Explore Google Clouds Generative AI tools and best practices.

This three-day instructor-led class introduces participants to the comprehensive and flexible infrastructure and platform services provided by Google Cloud Platform, with a focus on Compute Engine. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as networks, systems, and application services. This course also covers deploying practical solutions including securely interconnecting networks, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.

This course introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and other services provided by Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services, as well as networks and application services. This course also covers deploying practical solutions, including security and access management, resource management, and resource monitoring.

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.

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

This 2-day instructor-led course gives participants a broad study of networking options on Google Cloud Platform. Through presentations, demonstrations, and hands-on labs, learners explore and deploy GCP networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls, interconnection among networks, load balancing, Cloud DNS, and Cloud CDN. The course also covers common network design patterns and automated deployment using Cloud Deployment Manager.

This course gives participants broad study of security controls and techniques on Google Cloud Platform. Through lectures, demonstrations, and hands-on labs, participants explore and deploy the components of a secure GCP solution. Participants also learn mitigation techniques for attacks at many points in a GCP-based infrastructure, including Distributed Denial-of-Service attacks, phishing attacks, and threats involving content classification and use.

This course introduces participants to creating and deploying containerized applications on Google Kubernetes Engine (GKE). Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods and containers.

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

Learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. This course uses lectures, demos, and hands-on labs to show you how to use Google Cloud services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

This deep dive technical course provides technical training for Enterprise Data Warehouses (EDW) solutions using BigQuery.
The course provides guidance and training on technical challenges that can arise when migrating existing EDW and ETL pipelines to Google Cloud. Learners are provided with practical experience with real migration tasks, such as data migration, schema optimization, and SQL Query conversion and optimization.

The course also covers key aspects of ETL pipeline migration to Dataproc and using Pub/Sub, Dataflow, and Cloud Data Fusion.

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.

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 Cloud Bigtable for analysis. Learners get hands-on experience building streaming data pipeline components on Google Cloud by using QwikLabs.

The two main 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.

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 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 video lectures, demos, quizzes, and hands-on labs, the course demonstrates how to go from raw data to impactful visualizations and dashboards.

Dataform is a service for data analysts to develop, test, version control, and schedule complex SQL workflows for data transformation in BigQuery. In this course you will explore the components of Dataform core, learn how to define tables and dependencies in SQLX, document BigQuery tables and views, understand BigQuery security settings and how to manage these with Dataform, write assertions, execute SQL workflows, and explore additional advanced use cases.

This three-day instructor-led course teaches participants techniques for monitoring, troubleshooting, and improving infrastructure and application performance in Google Cloud. Guided by the principles of Site Reliability Engineering (SRE), and 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.

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.

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, next 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 PaLM API.

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 efficiency, 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.

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.

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.

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 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 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.

This course is an interactive learning experience designed to provide an actionable set of steps to help your organizations implement a Cloud Financial Management strategy to maximize your cloud investment with Google Cloud. In this class, you will walk through the five pillars of Google Cloud FinOps and understand how your organization can work to implement these pillars in their own FinOps approach.

Dive deep into the technical underpinnings of FinOps and gain the expertise to optimize your cloud spending and drive business value. This comprehensive course is designed for cloud architects, engineers, and technical professionals seeking to master the tools, technologies, and strategies that empower effective cloud financial management.

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.