Google Cloud

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.

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.

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.

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 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.

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.

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.