Statsmodel Forecast with Wallaroo Features

A life cycle with a Statsmodel forecast model from model creation to automation.

This tutorial series demonstrates how to use Wallaroo to create a Statsmodel forecasting model based on bike rentals. This tutorial series is broken down into the following:

  • Create and Train the Model: This first notebook shows how the model is trained from existing data.
  • Deploy and Sample Inference: With the model developed, we will deploy it into Wallaroo and perform a sample inference.
  • Parallel Infer: A sample of multiple weeks of data will be retrieved and submitted as an asynchronous parallel inference. The results will be collected and uploaded to a sample database.
  • External Connection: A sample data connection to Google BigQuery to retrieve input data and store the results in a table.
  • ML Workload Orchestration: Take all of the previous steps and automate the request into a single Wallaroo ML Workload Orchestration.

Statsmodel Forecast with Wallaroo Features: Model Creation

Training the Statsmodel to predict bike rentals.

Statsmodel Forecast with Wallaroo Features: Deploy and Test Infer

Deploy the sample Statsmodel and perform sample inferences.

Statsmodel Forecast with Wallaroo Features: Parallel Inference

Performing parallel inferences against the Statsmodel bike rentals model.

Statsmodel Forecast with Wallaroo Features: Data Connection

Using an external data connection for inference inputs and results with the bike rental prediction Statsmodel model.

Statsmodel Forecast with Wallaroo Features: ML Workload Orchestration

Automating the bike rental Statsmodel forecasting model.