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)
Creating ML Applications with Firebase

Introducing the Google Cloud Platform

The goal of this first introductory chapter is to give you an overview of the Google Cloud Platform (GCP). We start by explaining why machine learning (ML) and cloud computing go hand in hand as the demand for ever more hungry computing resources grows for today's ML applications. We then proceed with a 360° presentation of the platform's data-related services. Account and project creation as well as role allocation close the chapter.

A data science project follows a regular set of steps: in extracting the data, exploring, cleaning it, extracting information, training and assessing models, and finally building machine-learning-enabled applications. For each step of the data science flow, there are one or several services in the GCP that are adequate.

But, before we present the overall mapping of the GCP data-related services, it is important to understand why ML and cloud computing are truly made for each other.

In this chapter, we will cover the following topics:

  • ML and the cloud
  • Introducing the GCP
  • Data services of the Google platform