Jupyter notebooks are widely used by Datascientists for their data transformation workloads. There are scenarios where the notebook needs to be scheduled and run in regular interval and they need to be productionize. This repo provides a framework for the Datascientists to productionize their workloads dynamically (based on the notebooks) using AWS Stepfunctions with Service Integration to Sagemaker Processing Job.

A demo for scheduling a SageMaker Processing job with SageMaker Pipelines and Amazon EventBridge

Scheduled ML training + evaluation workflow on AWS using CDK (Python): SageMaker Training + Processing (evaluation) orchestrated by Step Functions, with S3 data, CloudWatch logs/alarms, and SNS failure alerts.

This study proposes a Climate-Aware Irrigation Scheduler designed to intelligently manage irrigation through the use of real-time data streams and cloud-based analytics. The system employs AWS IoT Core for data ingestion, AWS Lambda and Kinesis for stream processing, and Amazon SageMaker for machine learning-based irrigation prediction.