This project shows steps to build an end to end MLOps architecture that covers data prep, model training, realtime and batch inference, build model registry, track lineage of artifacts and model drift detection. It utilizes SageMaker Pipelines that offers machine learning (ML) to orchestrate SageMaker jobs and author reproducible ML pipelines. - View it on GitHub