Training the Statsmodel to predict bike rentals.
Statsmodel Forecast with Wallaroo Features
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.
Deploy the sample Statsmodel and perform sample inferences.
Performing parallel inferences against the Statsmodel bike rentals model.
Using an external data connection for inference inputs and results with the bike rental prediction Statsmodel model.
Automating the bike rental Statsmodel forecasting model.