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Hands-On Machine Learning on Google Cloud Platform

You're reading from   Hands-On Machine Learning on Google Cloud Platform Implementing smart and efficient analytics using Cloud ML Engine

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788393485
Length 500 pages
Edition 1st Edition
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Authors (3):
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Alexis Perrier Alexis Perrier
Author Profile Icon Alexis Perrier
Alexis Perrier
V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (18) Chapters Close

Preface 1. Introducing the Google Cloud Platform FREE CHAPTER 2. Google Compute Engine 3. Google Cloud Storage 4. Querying Your Data with BigQuery 5. Transforming Your Data 6. Essential Machine Learning 7. Google Machine Learning APIs 8. Creating ML Applications with Firebase 9. Neural Networks with TensorFlow and Keras 10. Evaluating Results with TensorBoard 11. Optimizing the Model through Hyperparameter Tuning 12. Preventing Overfitting with Regularization 13. Beyond Feedforward Networks – CNN and RNN 14. Time Series with LSTMs 15. Reinforcement Learning 16. Generative Neural Networks 17. Chatbots

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...

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