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

Further reading

Throughout the book, I will conclude the chapter with a list of online resources that recap or go beyond what was discussed in the chapter:

  • Many excellent articles on the GCP use for big data can be found on the Google big data blog at https://cloud.google.com/blog/big-data/.
  • What are the GCP services? Reto Meier, software engineer at Google, describes the different Google Cloud services in a simple way (for more information, see https://hackernoon.com/what-are-the-google-cloud-platform-gcp-services-285f1988957a). This is very useful for grasping the diversity of the GCP services.
  • An Annotated History of Google’s Cloud Platform is another post by Reto Meier on the history of the GCP. You can find it at: https://medium.com/@retomeier/an-annotated-history-of-googles-cloud-platform-90b90f948920. It starts with the bullet point: Pre 2008 — Computers invented. Google Founded.... A much more detailed timeline of the GCP is available on Crunchbase at https://www.crunchbase.com/organization/google-cloud-platform/timeline#/timeline/index.
  • The evolution of computing power, also known as Moore's law, is available at http://www.cs.columbia.edu/~sedwards/classes/2012/3827-spring/advanced-arch-2011.pdf, and a more recent version where the seven most recent data points are all NVIDIA GPUs is available at https://en.wikipedia.org/wiki/Moore%27s_law#/media/File:Moore%27s_Law_over_120_Years.png.
  • For more on the pricing war of the three main cloud platforms, see this article: Cloud Pricing Trends: Get the White Paper, Rightscale, 2013, at https://www.rightscale.com/lp/cloud-pricing-trends-white-paper.
  • A good article on Supercomputing vs. Cloud Computing by David Stepania can be found at https://www.linkedin.com/pulse/supercomputing-vs-cloud-computing-david-stepania/.