Distributed Machine Learning with Google Cloud ML




Create a deep neural network machine learning model

Add a wide and deep neural network model

Changing the learning rate

Deploying and using the Model

Distributed Machine Learning with Google Cloud ML

1 个小时 30 分钟 7 个积分


Google Cloud Self-Paced Labs


In this lab you will create and configure deep neural network models with Google Cloud ML, then use the Google Cloud ML Engine to make predictions using your trained models.

You will extend the basic Google Cloud ML machine learning framework developed in the previous lab in this quest, Machine Learning with TensorFlow, to explore a number of approaches to optimizing machine learning models.

The base data set that is used for these labs provides historic information about internal flights in the United States and has been retrieved from the US Bureau of Transport Statistics website. This data set can be used to demonstrate a wide range of data science concepts and techniques and is used in all of the other labs in the Data Science on the Google Cloud and Data Science on Google Cloud: Machine Learning quests. The specific data files used in this lab provide separate training and evaluation data sets. The details about how these files can be produced is covered in a previous lab in this quest, Processing Time Windowed Data with Apache Beam and Cloud Dataflow (Java).

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud. It runs on Compute Engine and connects to multiple cloud services such as BigQuery, Cloud SQL, or simple text data stored on Cloud Storage,so you can focus on your data science tasks.

BigQuery is a RESTful web service that enables interactive analysis of massively large datasets working in conjunction with Cloud Storage.


  • Extend a Python TensorFlow machine learning framework to use a deep neural network classifier

  • Modify the deep neural network classifier to implement a wide and deep model

  • Deploy a trained model to the Cloud ML Engine and make predictions using Python to execute API calls to the Cloud ML Engine

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