# MLOps Demo This repository contains a [notebook](WalkThrough.ipynb) to walk you through the main features of this demo, and [another one](DataScientist.ipynb) to exemplify the cod eexpected at the end of the development of a data science project. ## Use Case To demonstrate a typical Machine Learning flow, we will use an auto insurance domain to detect claims that are possibly fraudulent. More precisely we address the use-case: “what is the likelihood that a given auto claim is fraudulent?,” and explore the technical solution. ### Process flow and persona ![](imgs/Swimlane.drawio.png) ### Architecture The SageMaker project deploys 3 CodePipieline pipelines - Feature Ingestion - Model Building - Model Serving ![](imgs/architecture.drawio.png) ## CI/CD ![CI/CD diagram](imgs/cicd-diagram.drawio.png)