Book Image

Building Google Cloud Platform Solutions

By : Ted Hunter, Steven Porter, Legorie Rajan PS
Book Image

Building Google Cloud Platform Solutions

By: Ted Hunter, Steven Porter, Legorie Rajan PS

Overview of this book

GCP is a cloud computing platform with a wide range of products and services that enable you to build and deploy cloud-hosted applications. This Learning Path will guide you in using GCP and designing, deploying, and managing applications on Google Cloud. You will get started by learning how to use App Engine to access Google's scalable hosting and build software that runs on this framework. With the help of Google Compute Engine, you’ll be able to host your workload on virtual machine instances. The later chapters will help you to explore ways to implement authentication and security, Cloud APIs, and command-line and deployment management. As you hone your skills, you’ll understand how to integrate your new applications with various data solutions on GCP, including Cloud SQL, Bigtable, and Cloud Storage. Following this, the book will teach you how to streamline your workflow with tools, including Source Repositories, Container Builder, and Stackdriver. You'll also understand how to deploy and debug services with IntelliJ, implement continuous delivery pipelines, and configure robust monitoring and alerts for your production systems. By the end of this Learning Path, you'll be well versed with GCP’s development tools and be able to develop, deploy, and manage highly scalable and reliable applications. This Learning Path includes content from the following Packt products: • Google Cloud Platform for Developers Ted Hunter and Steven Porter • Google Cloud Platform Cookbook by Legorie Rajan PS
Table of Contents (29 chapters)
Title Page
Copyright and Credits
About Packt
Contributors
Preface
Index

Introduction


In this chapter, we'll discover Google Cloud's offering on machine learning (ML) and a few big data services. GCP offers two kinds of ML platform—one where we can use our own data to train models using services like Cloud ML Engine, and another to use already trained ML models for specific use cases like that of Cloud Natural Language, Translation API, Vision API, Speech API, and Video Intelligence. We'll focus more on machine learning APIs in this chapter.

GCP provides a wide range of services for end to end big data processing, of which we'll look at three services in particular—Google BigQuery, Cloud Dataflow, and Pub/Sub.

One of the biggest strengths of GCP is their big data services and ML capabilities. The technology behind these services existed in Google even before the arrival of the public cloud.