Serving real-time scikit-learn and XGBoost predictions

We’re excited to bring support for scikit-learn and XGBoost, machine learning libraries, to Google Cloud Platform and partner with a growing community of data scientists and ML users.

If you’ve built machine learning models with scikit-learn or XGBoost, and you want to serve your models in real time for an application, perhaps managing the resulting infrastructure sounds like a nightmare. Fortunately, now there’s an alternative. Serving your trained scikit-learn and XGBoost models on Cloud Machine Learning Engine (ML Engine) is now in beta.

You can now upload a model you’ve already trained onto Google Cloud Storage and use ML Engine’s online prediction service to support scalable prediction requests against your trained model.

In this post we’ll cover how to use a trained scikit-learn or XGBoost model on ML Engine to make online predictions. You can find the complete guides in our documentation with a sample model and dataset. If this is your first time using Google Cloud Platform (GCP), or you need help training a scikit-learn or XGBoost model, we suggest you follow one of the complete guides for scikit-learn or XGBoost.

How to bring your model to ML Engine

1. Save your model to a file
2. Upload the saved model to Google Cloud Storage
3. Create a model resource on ML Engine
4. Create a model version (linking your scikit-learn/XGBoost model)
5. Make an online prediction

Setup

• PROJECT_ID: Use the unique identifier that matches your GCP project
• MODEL_PATH: The path to your model on Google Cloud Storage
• MODEL_NAME: Use your own model name, such as ‘census’
• VERSION_NAME: Use your own version, such as ‘v1’
• REGION: Select a region here or use the default ‘us-central1’. The region is where the model will be deployed.
• INPUT_DATA_FILE: Include a JSON file that contains the data used as input to your model’s predict method.
  (For more info see the complete guide.)

1. Save your model

If you're using scikit-learn, you can save the model by adding the following code to the end of your file after your model is trained. This uses scikit-learn's built-in version of joblib.

If you're using XGBoost, there is also the option of saving the model using the native save_model method of the Booster object as explained here. Add this code to the end of your file to save your model:

2. Upload the saved model

scikit-learn

XGBoost

3. Create a model resource

In this step you’ll create a container that can hold several different versions of your actual model.

4. Create a model version

• name: The name specified for the version when it was created. This will be the VERSION_NAME variable you declared above.
• deploymentUri: The Google Cloud Storage location of the trained model used to create the version. This is the bucket where you uploaded the model with your MODEL_PATH.
• runtimeVersion: The Cloud ML Engine runtime version to use for this deployment. This is set to 1.4.
• framework: This specifies whether you’re using SCIKIT_LEARN or XGBOOST. This is set to SCIKIT_LEARN.
• pythonVersion - This specifies whether you’re using Python 2.7 or Python 3.5. The default value is set to “2.7”, if you are using Python 3.5, set the value to “3.5”

Note: It can take several minutes before your model becomes available.

You can check the status of your model’s deployment with:

5. Make an online prediction

Before you begin, you’ll need data you can send to your model in order to generate a prediction. Prepare a JSON file with the input data you want to use to make a prediction based on your model’s expected input. This file’s name should match the one you used above for the INPUT_DATA_FILE variable. (If you’re not sure what this looks like, check out the complete guide for more info.)

gcloud

In summary