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.
This lab gives you an introductory, end-to-end experience of training and prediction on Cloud Machine Learning Engine. The lab will use a census dataset to:
- Create a TensorFlow training application and validate it locally.
- Run your training job on a single worker instance in the cloud.
- Run your training job as a distributed training job in the cloud.
- Optimize your hyperparameters by using hyperparameter tuning.
- Deploy a model to support prediction.
- Request an online prediction and see the response.
- Request a batch prediction.
What you will build
The sample builds a wide and deep model for predicting income category based on United States Census Income Dataset. The two income categories (also known as labels) are:
- >50K — Greater than 50,000 dollars
- <=50K — Less than or equal to 50,000 dollars
Wide and deep models use deep neural nets (DNNs) to learn high-level abstractions about complex features or interactions between such features. These models then combine the outputs from the DNN with a linear regression performed on simpler features. This provides a balance between power and speed that is effective on many structured data problems.
The sample defines the model using TensorFlow's prebuilt
DNNCombinedLinearClassifier class. The sample defines the data transformations particular to the census dataset, then assigns these (potentially) transformed features to either the DNN or the linear portion of the model.
Join Qwiklabs to read the rest of this lab...and more!
- 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