Google Cloud Training
Architecting with Google Compute Engine
This three-day instructor-led class 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 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.
Introduction to Generative AI for Business Leaders
This course is designed for business leaders and decision-makers within organizations who are seeking to gain a comprehensive understanding of generative AI and its potential impact on their businesses. This workshop targets senior leaders from a variety of industries, including but not limited to technology, finance, healthcare, retail, and manufacturing.
Google Cloud Big Data and Machine Learning Fundamentals
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.
Getting Started with Google Kubernetes Engine
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.
Getting Started with Terraform for Google Cloud
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.
Introduction to Generative AI for Business Users
This course is tailored for professionals who are actively involved in executing and managing business processes within their organizations. This course is designed to empower participants with a foundational understanding of GenAI and GenAI tools on Google Cloud, enabling them to identify opportunities to leverage GenAI tools and platforms within their specific roles and functions.
Industry Use Cases for Generative AI
This course is designed for business professionals and technical individuals who are interested in understanding the practical applications of generative AI in various industries. This course targets professionals across different roles, including business analysts, data scientists, software developers, business users and decision-makers, who are specifically interested in leveraging generative AI for business solutions.
Generative AI Explorer
Explore Vertex AI Studio and the PaLM API in Vertex AI, all while taking part in a friendly competition! You will create and test a prompt, create a conversation, and explore the prompt gallery through hands-on activities. After completing these tasks, you will compete to be the first to finish a challenge lab using what you have learned from earlier labs.
Interactive Chat for Applications Using Vertex AI Studio
Generative AI is being used to develop new products and services across multiple industries, such as personalized marketing communications, chatbots for interacting with customers, and virtual assistants. For example, It can also be used to create chatbots that can answer customer questions and provide customer support.
Vertex AI Model Garden
Vertex AI Model Garden provides enterprise-ready foundation models, task-specific models, and APIs. Model Garden can serve as the starting point for model discovery for various different use cases. You can kick off a variety of workflows including using models directly, tuning models in Vertex AI Studio, or deploying models to a data science notebook.
Introduction to Responsible AI in Practice
The development of AI has created new opportunities to improve the lives of people around the world, from business to healthcare to education. It has also raised new questions about the best way to build fairness, interpretability, privacy, and safety into these systems.
Architecting with Google Cloud: Design and Process
This two-day instructor-led class equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine course and assumes hands-on experience with the technologies covered in either of those courses.
Logging, Monitoring, and Observability in Google Cloud
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.
Course 765: Google Cloud Advanced Skills & Certification Workshop: Associate Cloud Engineer (ACE)
This workshop is designed to help IT professionals prepare for the Google Cloud Certified – Associate Cloud Engineer exam. In this workshop, we review the exam guidelines and cover the main topics you may be tested on. Where the Professional Cloud Architect exam assesses your ability to design and architect solutions on Google Cloud, the Associate Cloud Engineer exam assesses your ability to deploy and manage solutions on Google Cloud. In a nutshell, a cloud architect designs things and a cloud engineer does things. The exam, and hence this course, focus on the tasks required to deploy, manage, and monitor applications and data using Google Cloud services.
Course 795: Google Cloud Advanced Skills & Certification Workshop: Professional Cloud Architect
Google Cloud Certification Training Description This workshop is designed to help IT professionals prepare for the Google Certified Professional – Cloud Architect Certification Exam. In this workshop, we review the exam guidelines, product strategies for the major Google Cloud services, and other related topics covered by the exam. We examine concepts related to Site Reliability Engineering, and review cloud architectures, implementations, and best practices to solve common problems.
Developing Applications with Cloud Functions on Google Cloud
In this course, you learn about Cloud Functions, Google’s serverless, fully-managed functions as a service (FaaS) product. With Cloud Functions, you implement single-purpose functions that respond to HTTP requests and process events from your cloud infrastructure.
Course 766: Google Cloud Advanced Skills & Certification Workshop: Professional Cloud Developer
This workshop is designed to help IT professionals prepare for the Google Professional Cloud Developer certification exam. In this workshop, we review the exam guidelines and cover the main topics you may be tested on.
From Data to Insights with Google Cloud Platform
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 three-day instructor-led class teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.
Data Engineering on Google Cloud
This four-day instructor-led class provides 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.
Serverless Data Processing with Dataflow
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
Enterprise Database Migration
This course is intended to give architects, engineers, and developers the skills required to help enterprise customers architect, plan, execute, and test database migration projects. Through a combination of presentations, demos, and hands-on labs participants move databases to GCP while taking advantage of various GCP services. This course covers how to move on-premises, enterprise databases like SQL Server to Google Cloud (Compute Engine and Cloud SQL) and Oracle to Google Cloud bare metal.
Course 796: Google Cloud Advanced Skills & Certification Workshop: Professional Data Engineer
This workshop is designed to help IT professionals prepare for the Google Cloud Certified – Professional Data Engineer exam. In this workshop, we review the exam guidelines and product strategies for the major Cloud Storage, big data, and analytics services covered by the exam. We examine concepts related to data transformation, real-time processing, visualization, machine learning, and best practices to solve common problems.
Analyzing and Visualizing Data in Looker
In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done only by SQL-savvy developers or analysts. Upon completion of this course, you will be able to leverage Looker’s modern analytics platform to find and explore relevant content in your organization’s Looker instance, ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision making.
Developing Data Models with LookML
This course empowers you to develop scalable, performant LookML (Looker Modeling Language) 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.
Looker Developer Deep Dive
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 lecture and independent exercises using sample data.
Data Warehousing with BigQuery: Storage Design, Query Optimization, and Administration
In this course, you learn about the internals of BigQuery and best practices for designing, optimizing, and administering your data warehouse. Through a combination of lectures, demos, and labs, you learn about BigQuery architecture and how to design optimal storage and schemas for data ingestion and changes. Next, you learn techniques to improve read performance, optimize queries, manage workloads, and use logging and monitoring tools. You also learn about the different pricing models. Finally, you learn various methods to secure data, automate workloads, and build machine learning models with BigQuery ML.
Migrating Amazon Athena Users to BigQuery and Dataproc
In this course, you will learn how to translate various concepts in Amazon Athena to the analogous concepts in BigQuery and Dataproc. You will learn how the high-level storage and compute architectures of Amazon Athena compare to BigQuery and Dataproc, understand how to configure datasets and tables in BigQuery, understand schema mappings from Amazon Athena to BigQuery and schema optimization in BigQuery. You will also learn how to create ephemeral Dataproc clusters for Spark data processing jobs and best practices around resource management for these jobs.
Migrating Amazon Redshift Users to BigQuery
In this course, you will learn how to translate various concepts in Amazon Redshift to the analogous concepts in BigQuery. You will learn how the high-level architectures of Amazon Redshift and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Amazon Redshift to data types in BigQuery, understand schema mapping from Amazon Redshift to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Amazon Redshift and BigQuery.
Migrating Snowflake Users to BigQuery
In this course, you will learn how to translate various concepts in Snowflake to the analogous concepts in BigQuery. You will learn how the high-level architectures of Snowflake and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Snowflake to data types in BigQuery, understand schema mapping from Snowflake to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Snowflake and BigQuery.
Migrating Teradata Users to BigQuery
In this course, you will learn how to translate various concepts in Teradata to the analogous concepts in BigQuery. You will learn how the high-level architectures of Teradata and BigQuery compare, understand differences in how to configure datasets and tables, map data types in Teradata to data types in BigQuery, understand schema mapping from Teradata to BigQuery, optimize your new schemas in BigQuery, and do a high-level comparison of SQL dialects in Teradata and BigQuery.
Managing a Data Mesh with Dataplex
Dataplex is an intelligent data fabric that enables organizations to centrally discover, manage, monitor, and govern their data across data lakes, data warehouses, and data marts to power analytics at scale. Specifically, you can use Dataplex to build a data mesh architecture, which is an organizational and technical approach that decentralizes data ownership among domain data owners.
Architecting with Google Kubernetes Engine
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.
Architecting Hybrid Cloud Infrastructure with Anthos
This four-day, instructor-led course prepares students to modernize, manage, and observe their containerized applications using Kubernetes, in Google Cloud, AWS, Azure, and on-premises. Through presentations and hands-on labs, participants explore Google Kubernetes Engine (GKE), Connect Agent, Anthos Service Mesh, and Anthos Config Management features. Participants learn how to work with containerized applications even when split between multiple clusters, hosted by multiple cloud providers or on-premises. This course is a continuation of Architecting with GKE and assumes direct experience with the technologies covered in that course.
Cloud Digital Leader
If you’re wondering how the cloud can transform your business, then the Cloud Digital Leader (CDL) 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.
Leading Change When Moving to Google Cloud
Moving to the cloud can bring tremendous business benefits, but it’s also a big change for your employees and requires the right management approach. In this course, you’ll learn how to lead your team through a Google Cloud migration so that you can realize the platform’s full potential. You’ll explore ways to lead by example and the psychology behind people’s reactions to change. You’ll also learn about the framework Google Cloud has created to help you manage this change so that you can inspire your team and move them toward your cloud goals.
Managing Machine Learning Projects with Google Cloud
Business professionals in non-technical roles have a unique opportunity to lead and influence machine learning projects. In this course, you’ll explore machine learning without the technical jargon. You’ll learn how to translate business problems into custom machine learning use cases, assess each phase of the project, and translate the requirements to your technical team.
Data-Driven Transformation with Google Cloud
Designed for leaders and business decision-makers, this course will help you explore opportunities the cloud can provide to transform your business. You’ll learn how cloud technology, data, and machine learning applications are helping businesses reimagine their daily work. You’ll uncover new possibilities in your data strategy and learn to think like a data engineer. You’ll also learn how to translate business challenges into machine learning use cases and vet them for feasibility and impact.
Networking in Google Cloud
This 2-day instructor-led course builds on the networking concepts covered in the Architecting with Google Compute Engine 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.
Security in Google Cloud
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.
Course 767: Google Cloud Advanced Skills & Certification Workshop: Professional Cloud Security Engineer
This workshop is designed to help IT professionals prepare for the Google Professional Cloud Security Engineer certification exam. In this workshop, we review the exam guidelines and cover the main topics you may be tested on.
Machine Learning on Google Cloud
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.
Introduction to AI and Machine Learning on Google Cloud
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 based on the different goals of users, including data scientists, AI developers, and ML engineers.
Customer Experiences with Contact Center AI – Dialogflow ES
In this course, learn how to design customer conversations using Contact Center Artificial Intelligence (CCAI). You’ll use Dialogflow ES to create virtual agents and test them using the simulator. Learn to add functionality to access data from external systems, making virtual agents conversationally dynamic. You’ll be introduced to testing methods, connectivity protocols, APIs, environment management, and compliance measures. Learn best practices for integrating conversational solutions with your existing contact center software and implementing solutions securely and at scale.
Developing APIs with Google Cloud’s Apigee API Platform
In this course, you learn how to design APIs, and how to use OpenAPI specifications to document them. You learn about the API life cycle, and how the Apigee API platform helps you manage all aspects of the life cycle. You learn how APIs can be designed using API proxies, and how APIs are packaged as API products to be used by app developers.
Managing Google Cloud’s Apigee API Platform for Hybrid Cloud
Learn how to install and manage Google Cloud’s Apigee API Platform in a hybrid cloud. This course uses lectures, hands-on labs, and supplemental resources to show you how to design, install, manage, and scale your Apigee API Platform.
Installing and Managing Google Cloud’s Apigee API Platform for Private Cloud
This course introduces you to the fundamentals and advanced practices used to install and manage Google Cloud’s Apigee API Platform for Private Cloud. Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, install, secure, manage, and scale the Apigee API Platform for Private Cloud.
Building Solutions with Apigee X
In this course, you will go through the Apigee journey as a product lead for a new application. You will take roles on the development and security teams. You will take an API originally designed for an on-premises legacy application use and modernize it for the cloud using Apigee X. You will follow Google Cloud best practices alongside Apigee X to design a secure, scalable and resilient platform for your company’s needs.
