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

Creating a bucket in Google Cloud Storage

As we said previously, a bucket is a container that holds your data. Everything saved in Google Storage must be contained within a bucket, corresponding to the folders. You can use them to organize and control access to data, but, unlike folders, you can not create sub-buckets. In Google Storage, individual data is saved in the form of objects. Such objects can be files of any type, extension, and size; tables created with BigQuery are also considered objects, as we will see in Chapter 4, Querying Your Data with BigQuery. All objects related to a single job must be contained in a bucket.

Objects are immutable, so an object cannot be edited directly in Google Storage. It is important to specify that it is not possible to make any kind of changes to the content of an object; if you want to modify an object stored in Google Storage it can...