Book Image

Hands-On Machine Learning on Google Cloud Platform

By : Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
Book Image

Hands-On Machine Learning on Google Cloud Platform

By: Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier

Overview of this book

Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.
Table of Contents (18 chapters)
8
Creating ML Applications with Firebase

Introducing the GCP

The first cloud computing service dates back to 15 years ago, when, in July 2002, Amazon launched the AWS platform to expose technology and product data from Amazon and its affiliates, enabling developers to build innovative and entrepreneurial applications on their own. In 2006, AWS was relaunched as the EC2.

The early start of AWS gave Amazon a lead in cloud computing, one that has never faltered since. Competitors were slow to counteract and launch their own offers. The first alternative to the AWS cloud services from a major company came with the Google App Engine launched in April 2008 as a PaaS service for developing and hosting web applications. The GCP was thus born. Microsoft and IBM followed, with the Windows Azure platform launched in February 2010 and LotusLive in January 2009.

Google didn’t enter the IaaS market until much later. In 2013, Google released the Compute Engine to the general public with enterprise service-level agreements (SLA).

Mapping the GCP

With over 40 different IaaS, PaaS, and SaaS services, the GCP ecosystem is rich and complex. These services can be grouped into six different categories:

  • Hosting and computation
  • Storage and databases
  • Networking
  • ML
  • Identity and security
  • Resource management and monitoring

In the following section, we learn how to set up and manage a single VM instance on Google Compute Engine. But, before that, we need to create our account.