Cloud ML Engine: Qwik Start
This lab will give you hands-on practice with TensorFlow model training, both locally and on Cloud ML Engine. After training, you will learn how to deploy your model to Cloud ML Engine for serving (prediction). You'll train your model to predict income category of a person using the United States Census Income Dataset.
What you will use:
In this lab, you will get hands-on practice with the following tools and services:
- TensorFlow, which is an open source library for numerical computation, specializing in machine learning applications.
- Cloud Machine Learning Engine, which brings the power and flexibility of TensorFlow to the cloud, letting you perform large scale training on a managed cluster, and then scalably server your trained model for prediction.
- To build your model, you'll use TensorFlow's prebuilt DNNCombinedLinearClassifier model is sometimes called ‘Wide & Deep'
What you will learn:
- How to build a TensorFlow model, and how to train it both locally and in the cloud using Cloud ML Engine (CMLE).
- How to use your trained model for prediction, and how to use CMLE's online prediction service.
What you need:
A basic familiarity with Python and Linux commands will be helpful, but not necessary.
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- Get temporary access to the Google Cloud Console.
- Over 200 labs from beginner to advanced levels.
- Bite-sized so you can learn at your own pace.
Set up a Google Cloud Storage bucket
Upload the data files to your Cloud Storage bucket
Run a single-instance trainer in the cloud
Create a Cloud ML Engine model
Create a version v1 of your model