This CDK implementation demonstrates how MLOps workflow for Time Series Forecasting with SageMaker Built-in DeepAR can be automated with AWS Serverless Services.

AWS CDK v2 is required to deploy this application. See this page to install AWS CDK in TypeScript.

For demonstration, this pipeline has been implemented for an use case for SageMaker Built-in DeepAR from SageMaker sample notebook. In this example, the user is building a DeepAR model with UCI Electricity Dataset.

This CDK application will deploy a MLOps Pipeline for SageMaker Built-in DeepAR. The pipeline automatically performs preprocessing, model training, model creation, batch transformation, and post processing. This is common process for building a ML model for timeseries forecasting, and the MLOps pipeline created by this CDK application automates it.Each of these operations are implemented and orchestrated with AWS Step Functions.

The pipeline consist of the following steps:

Implemented in Glue Python Job. Performs data operations defined in the python script glue/preprocess.py. It downloads the dataset .zip from the resource bucekt, and then process it to train a DeepAR model. The current code contains preprocessing logic for the sample dataset explained above. The script applies same data manipulation defined in the sample notebook. For your own dataset, you can implement your own preprocessing script using pandas, NumPy, sklearn, etc.

Creates a SageMaker Training Job with the dataset files created at the Preprocess Step.

Your may have to change training instance type, training hyperparameters and SageMaker DeepAR Image URI denoted in construct/state-machine.ts.

If you want to use different instance type for your training job, please fix it.

If you are running this pipeline in other region than ap-norteast-2, you have to set SageMaker DeepAR Image URI to a right value, since [SageMaker DeepAR Image URI] can differ. You can find out the list of SageMaker DeepAR Image URIs here.

If you have your own hyperparamert set for your own dataset, please change them as well.

Creates a SageMaker Model from the model artifacts (which contains weights, model meta data) created from the Train Step.

Creates a SageMaker Bratch Transformation Job with the trained model to infer future time series.

If you want to use different instance type for your batch transformation job, please fix it.

Once Transform Step has been completed, you will find out the inferred values in the resource bucket under /json/forecast.

Implemented in Glue Python Job. Performs data operations defined in the python script glue/postprocess.py. Since inference results are created in json format by SageMaker Built-in DeepAR, the pipeline will convert to a friendly csv format. Just like preprocess step, you can add your own postprocessing logic in this step too, using pandas, NumPy, sklearn, etc.

Note, this part explains how to run the pipeline with the sample dataset introduced above.

You can download the sample dataset by executing the script given in this project like below.

cd sample-data
python download.py

Once done, you will find out LD2011_2014.txt.zip under sample-data/ directory.

Run the below command on the root of this project to deploy the pipeline.

cdk deploy DeeparMlopsPipelineCdkStack

If successfully deployed, we are ready to launch 🚀🚀🚀

Once you deployed the pipeline, you will find an S3 bucket named deepar-mlops-pipeline-resource-{YOUR-12DIGIT-AWS-ACCOUNT-ID}. We will call this bucket the resource bucket from now on. All you have to do now is to upload the dataset file to trigger the pipeline.

The pipeline execution starts with the Lambda function named DeepAR-MLOps-Pipeline-Upload-Trigger. This Lambda function listens to the resource bucket and gets triggered if an object is created under raw/*.zip path.

Now, upload the dataset .zip file to execute this Lambda function and launch the MLOps Pipeline.

aws s3 cp LD2011_2014.txt.zip s3://deepar-mlops-pipeline-resource-{YOUR-12DIGIT-AWS-ACCOUNT-ID}/raw

Navigate to the StepFunctions console to monitor the excecution. You will see the progress like below while execution.

When all the steps are completed, the resource bucket will have the following structure:

raw/                    # Where you uploaded the raw dataset
json/                   # Where preprocessed json files for SageMaker Built-in DeepAR are stored
model/                  # Where SageMaker model artifacts (weights, model metadata, etc.) generated after training are stored
forecast/               # Where csv files that has future forcast values are stored

This project follows typical AWS CDK project structure. The below directories are important from the user perspective.

bin/                    # Entrypoint of the entire project
lib/                    # CDK Constructs and Stacks are defined
lambda/                 # Python scripts for Lambda Functions
glue/                   # Python scripts for Glue Jobs

Customize this pipeline by implementing your own preprocessing/postprocessing steps with your own dataset. And don't forget to change the hyperparameters and SageMaker DeepAR Image URIs for your own enviroment!

Please open an issue to let the maintainer know. Or PR would be also appreciated. The maintainer will reach out!

See CONTRIBUTING for more information.

This library is licensed under the MIT-0 License. See the LICENSE file.