Google Cloud Platform (GCP) is not only the most popular cloud offering currently, but it is possibly the best cloud offering for high-end machine learning applications thanks to TensorFlow, the popular deep learning technology from Google. This course will help you to learn the essential concepts that are needed to deploy TensorFlow applications on GCP. The course starts with an introduction to cloud computing, Hadoop, and GCP and helps you in setting up the lab for exercises. You’ll understand various compute options, such as Google Compute Engine (GCE), and explore different storage options. As you advance, you’ll work with Cloud SQL and get an overview of BigTable and BigQuery by performing lab exercises, explore the data flow feature called Apache Beam, and use Data Proc for managing Hadoop. You’ll also learn how to use Pub/Sub on GCP and explore the features of a data lab. The course will then take you through machine learning and TensorFlow concepts and show you how to prepare a dataset to run a model and how to work with virtual machines and images. Finally, you will become familiar with networking and security concepts and get to grips with the basics of Hadoop. By the end of this course, you’ll have developed the skills required to build TensorFlow and machine learning models on GCP. The entire code bundle for this course can be found at: https://github.com/packtpublishing/gcp-complete-google-data-engineer-and-cloud-architect-guide-v-