About This Architecture

End-to-end MLOps pipeline orchestrating model training, experiment tracking, and deployment across EC2, SageMaker, ZenML, and MLflow on AWS. Researchers develop on EC2 t3.xlarge workbenches, triggering ZenML-orchestrated pipelines that execute SageMaker training jobs against curated S3 datasets while logging experiments to MLflow. Trained models flow through SageMaker Model Registry into real-time endpoints and batch transform jobs, with CloudWatch monitoring production performance. This architecture decouples experiment tracking (MLflow) from orchestration (ZenML) and training infrastructure (SageMaker), enabling reproducible, scalable ML workflows. Fork this diagram on Diagrams.so to customize compute instance types, add data validation steps, or integrate additional monitoring tools. The three-tier S3 structure (raw, curated, aggregated) enforces data governance best practices across the pipeline.

People also ask

How do I build a production MLOps pipeline on AWS that combines ZenML orchestration with SageMaker training and MLflow experiment tracking?

This diagram shows a complete MLOps workflow where researchers on EC2 t3.xlarge workbenches trigger ZenML-orchestrated pipelines that execute SageMaker training jobs, log experiments to MLflow, and deploy models via SageMaker endpoints. The architecture separates concerns: ZenML handles orchestration, SageMaker manages training and serving, MLflow tracks experiments, and tiered S3 buckets enforce