The purpose of the labs is to demo how to use Amazon SageMaker built-in algorithm DeepAR to do time series data forecasting. Besides a notebook to walk through the steps, we provide ml pipeline creation reference.

There are two Jupyter Notebooks;

1. Forecasting Air Quality with Amazon SageMaker and DeepAR to demo time series data forecasting.
2. Air Quality Forecasting ML Pipeline (manual) to demo ML Pipeline manual creation.

Especially, the second notebook demo how to manually create a ML Pipeline for the air quality forecasting.

The pipeline design is inspired by Amazon SageMaker Safe Deployment Pipeline.

• With default CFN parameter values setup during CFN stack creation, GitHub Repo - timeseries_blog will be mirrored to CodeCommit repo so that user can experiment code change to trigger pipeline easily.
• CodePipeline pipeline orchestrates the build process with CodeBuild project.
• CodeBuild project process preprocess container build and ML Pipeline creation & execution with Step Functions state machine. (the workflow won't be part of CFN stack, hence, you may manually remove it while deleting CFN stack.)
• State machine demo ML pipeline and orchestrate data preprocessing, model training/tuning and batch transform.
• Amazon SageMaker Notebook instance can be used to explore notebooks.

For ML Pipeline Process, Refer to