Google Cloud Training Courses

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.

In this course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

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 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.

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 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 teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker0; use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. Learn all this and more!

This course will give you hands-on experience optimizing, deploying, and scaling a variety of production ML models. You’ll learn how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text, along with recommendation systems.

This two-day course series builds on the networking concepts covered in the Networking in Google Cloud quest. Through recorded lectures, 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, and private connection options. The course also covers common network design patterns, billing, pricing, monitoring, and troubleshooting.

This training course gives you a broad study of security controls and techniques in Google Cloud. Through lectures, demonstrations, and hands-on labs, you’ll explore and deploy the components of a secure Google Cloud solution, using services like Cloud Identity, Identity and Access Management (IAM), Cloud Load Balancing, Cloud IDS, Web Security Scanner, BeyondCorp Enterprise, Cloud DNS, and much more.

This two-day instructor-led Networking in Google Cloud Platform class gives participants broad study of networking options on Google Cloud. Through a combination of presentations, demonstrations, and hands-on labs, participants explore and deploy Google Cloud networking technologies, such as Google Virtual Private Cloud (VPC) networks, subnets, firewalls; interconnection among networks; load balancing; Cloud DNS; Cloud CDN; Cloud NAT. The course will also cover common network design patterns and automated deployment using Deployment Manager or Terraform.

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

In this Developing Applications with Google Cloud Platform course, application developers learn how to design, develop, and deploy applications that seamlessly integrate components from the Google Cloud ecosystem. Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.

This course introduces students to fundamentals, practices, capabilities and tools applicable to modern cloud-native application development using Google Cloud Run. Through a combination of lectures, hands-on labs, and supplemental materials, students will learn how to design, implement, deploy, secure, manage, and scale new (greenfield) and existing (brownfield) applications on Google Cloud using Cloud Run.

This Data Engineering on Google Cloud Platform course is designed to provide participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data.

Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This From Data to Insights with Google Cloud Platform specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL.

This course teaches you how to build Vertex AI AutoML models without writing a single line of code; build BigQuery ML models knowing basic SQL; create Vertex AI custom training jobs you deploy using containers (with little knowledge of Docker0; use Feature Store for data management and governance; use feature engineering for model improvement; determine the appropriate data preprocessing options for your use case; write distributed ML models that scale in TensorFlow; and leverage best practices to implement machine learning on Google Cloud. Learn all this and more!

This course teaches AWS professionals about the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for AWS system administrators, solutions architects, and SysOps administrators who are familiar with AWS features and setup and want to gain experience configuring Google Cloud products immediately. This course uses lectures, demos, and hands-on labs to show you the similarities and differences between the two platforms and teach you about some basic tasks on Google Cloud.

This course teaches Azure professionals about the core capabilities of Google Cloud in the four technology pillars: networking, compute, storage, and database. It is designed for Azure system administrators, solutions architects, and SysOps administrators who are familiar with Azure features and setup and want to gain experience configuring Google Cloud products immediately. This course uses lectures, demos, and hands-on labs to show you the similarities and differences between the two platforms and teach you about some basic tasks on Google Cloud.

This one-day instructor-led Getting Started with Google Kubernetes Engine class equips students to containerize workloads in Docker containers, deploy them to Kubernetes clusters provided by Google Kubernetes Engine, and scale those workloads to handle increased traffic. Students also learn how to continuously deploy new code in a Kubernetes cluster to provide application updates.

This Architecting with Google Kubernetes Engine course introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, students 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 Google Cloud Platform Big Data and Machine Learning Fundamentals course introduces students to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities.

If you’re wondering what the machine learning hype is about or want to know what it can do for your enterprise, without the technical jargon, this course is for you. In this Machine Learning for Business Professionals course students will learn what machine learning is, how to translate business problems into machine learning use cases, how to vet those use cases for feasibility and impact, how to discover unexpected use cases, how to carry a machine learning project through its various phases, and how to pursue machine learning and artificial intelligence responsibly and ethically.

This course will familiarize students with Google Cloud’s flexible infrastructure and platform services, with a specific focus on Compute Engine. This course uses a combination of lectures, demos, and hands-on labs to explore and deploy solution elements, including infrastructure components like networks, systems, and application services. They’ll also learn how to deploy practical solutions such as hybrid networking, customer-supplied encryption keys, security and access management, quotas and billing, and resource monitoring.

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 Managing Google Cloud’s Apigee API Platform for Hybrid Cloud course introduces students to fundamentals and advanced practices applicable to the installation and management of Google Cloud’s Apigee API Platform for hybrid cloud. Through a combination of lectures, hands-on labs, and supplemental materials, students will learn how to design, install, secure, manage, and scale Apigee API Platform.

Learn the fundamentals of API Design and the out-of-the-box capabilities offered by Google Cloud’s Apigee API Platform. This course features a combination of lectures, hands-on labs, and supplemental materials to show you how to design, build, secure, deploy, and manage API solutions.

This course teaches students about deploying and managing Microsoft Windows® workloads on Google Cloud. This course uses lectures and hands-on labs to show students how to plan and configure Microsoft Windows Server and Microsoft SQL Server in Google Cloud. They will configure identity solutions including Managed Service for Microsoft Active Directory, deploy Windows workloads to Compute Engine and Google Kubernetes Engine, and learn to manage and operate Windows workloads with Cloud Console, Cloud Logging, and Cloud Monitoring.