Google Cloud

This certification & training course uses lectures, demos, and hands-on labs to give you an overview of Google Cloud products and services so that you can learn the value of Google Cloud and how to incorporate cloud-based solutions into your business strategies

If you’re wondering how cloud can transform your business then this course is for you. Aimed at business leaders new to cloud, this GCP-CDL: Cloud Digital Leader training with Google Cloud course provides insight into Google’s approach to digital transformation, and how to embrace cloud in your business. This training is designed to give you foundational knowledge about cloud technology, data, and Google Cloud products that enable digital transformation. Empowering you and your team(s) to contribute to cloud-related business initiatives in your organization.

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.

Introduction to Google Workspace Administration is the first course in the Google Workspace Administration series of courses. This series will serve as the starting place for any new Google Workspace admin as they begin their journey of managing and establishing Google Workspace best practices for their organization. These courses together will leave you feeling confident to utilize the basic functions of the admin console to manage users, control access to services, configure security settings, and much more. Through a series of readings and step-by-step hands-on exercises, and knowledge checks, learners can expect to leave this training with all of the skills they need to get started as Google Workspace administrators. In this course you will sign up for a Google Workspace account and configure your DNS records for Google Workspace. You will learn how to provision and manage your users, and will create groups and calendar resources for your organization. You will be introduced to your Cloud Directory and will learn how to split your organization into organizational units to simplify user and service management. Finally you will learn how to delegate admin privileges to other users in your organization.

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 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 and managing Google Cloud resources using Terraform.

This certification & training 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 certification & training 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.

If you are new to Google Workspace, this certification & training course will equip you with the skills you need to be productive in the workplace. Through a series of lectures, demonstrations, and hands-on activities, you will become proficient in the use of the following core Google Workspace applications: Gmail, Google Calendar, Google Drive, Google Docs, Google Sheets, Google Slides, Google Meet and Google Chat.

G Suite just got better — introducing Google Workspace. Content delivered by our partners may still include some references to G Suite as updates are being made but you will still receive relevant training. If you're a Google Workspace admin who needs to manage and establish Google Workspace best practices for your organization, the lectures and hands-on labs in this certification & training course will show you how to use the features in the admin console so that you can manage users, control access to services, configure security settings, monitor Google Workspace operations, and more.

What is machine learning, and what kinds of problems can it solve? Why are neural networks so popular right now? How can you improve data quality and perform exploratory data analysis? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent? In this certification & training course, you'll learn how to write distributed machine learning models that scale in Tensorflow 2.x, perform feature engineering in BQML and Keras, evaluate loss curves and perform hyperparameter tuning, and train models at scale with Cloud AI Platform.

In this certification & training course, you will learn about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. The first few modules discuss pipeline components, pipeline orchestration with TFX, how you can automate your pipeline through CI/CD, and how to manage ML metadata. Then we will discuss how to automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use Cloud Composer to orchestrate your continuous training pipelines, and MLflow for managing the complete machine learning life cycle.

This certification & training course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models

Logging, Monitoring and Observability in Google Cloud. This certification & training courses 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.

Many IT organizations experience a disconnect between developers, who focus on agility, and operators, who focus on stability. Site Reliability Engineering (SRE) is how Google bridges the gap between development and operations, while also providing mission-critical production support. In this certification & training course, you'll learn the fundamentals and best practices of SRE, the importance of adopting an SRE culture, and how SRE can improve collaboration between IT and business leaders—and help the entire organization succeed.

Learn how to create and deploy containerized applications on Google Kubernetes Engine (GKE). This certification & training course features a combination of lectures, demos, and hands-on labs to help you explore and deploy solution elements —including infrastructure components like pods, and containers.

This certification & training 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.

What is machine learning, and what kinds of problems can it solve? What are the five phases of converting a candidate use case to be driven by machine learning, and why is it important that the phases not be skipped? Why are neural networks so popular now? How can you set up a supervised learning problem and find a good, generalizable solution using gradient descent and a thoughtful way of creating datasets? Our certification & training courses teaches you how to write distributed machine learning models that scale in Tensorflow 2.x, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud.

Discover how to modernize, manage, and observe applications using Kubernetes—whether the application is deployed on-premises or on Google Cloud. This certification & training course uses lectures and hands-on labs to help you explore and deploy using Kubernetes Engine (GKE), GKE Connect, Istio service mesh, and Anthos Config Management capabilities that will enable you to work with modern applications, even when they are split among multiple clusters hosted by multiple providers, or on-premises. This is a continuation of Architecting with GKE, and you'll need hands-on experience with the technologies covered in that course in order to benefit from this course.