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

Run Job

We have planned several operations on our database: it is time to make these changes. To do this, just click on Run Job on the Transformer page. In this way, the Run Job page will be open, where we can specify transformation and profiling jobs for the currently loaded dataset. Available options include output formats and output destinations.

The Profile Results option allows us to generate a visual result profile. The visual profile is very useful for examining the problems of our recipe and iterating, even if it is a process that requires a lot of resources. If the dataset we are processing is large, disabling the profiling of the results can improve the overall execution speed of the job.

After setting the available options correctly, we can queue the specified job for execution by simply clicking Run Job. Once this is done, the job is queued for processing. At the end...