Distributed Machine Learning with Google Cloud ML
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 Platform and Data Science on Google Cloud Platform: 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 Platform. It runs on Google Compute Engine and connects to multiple cloud services such as Google BigQuery, Cloud SQL, or simple text data stored on Google Cloud Storage,so you can focus on your data science tasks.
Google BigQuery is a RESTful web service that enables interactive analysis of massively large datasets working in conjunction with Google 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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- 获取对“Google Cloud Console”的临时访问权限。
- 200 多项实验，从入门级实验到高级实验，应有尽有。
Check for successful run of a gcloud ml-engine job called dnn-*
Check for successful run of a gcloud ml-engine job called wide-*
Check for successful run of a gcloud ml-engine job called learn-*
Check for ml-engine model called flights