AWS Real-Time ML Streaming Platform is a local-first simulation of a production-style streaming ML architecture. It demonstrates event generation, validation, feature processing, inference, anomaly detection, routing, alerting, monitoring, and drift/evaluation readiness.
- Data Scientist
- Machine Learning Engineer
- AI Engineer
- MLOps Engineer
- Data Engineer with ML platform focus
- Python engineering
- Streaming analytics design
- Feature engineering
- Inference contract design
- Anomaly detection
- Monitoring and reporting
- Drift detection
- MLOps readiness
- AWS architecture mapping
- Testing and CI
For Data Scientist roles, the project shows feature design, evaluation thinking, anomaly labels, and model-quality metrics.
For ML Engineer roles, it shows modular pipeline design, reproducible scoring, monitoring, routing, and local interfaces that can map to cloud services.
For AI Engineer roles, it shows AI system evaluation readiness, operational thinking, and production-style architecture decomposition.
The platform simulates how a business can identify risky events, operational anomalies, alert-worthy incidents, and model-quality issues before they become stale.
The project shows how to build a testable local architecture that can later evolve toward AWS deployment.
Local-first AWS-oriented real-time ML streaming platform simulation with event generation, validation, feature processing, inference, anomaly detection, routing, alerts, monitoring, and drift evaluation.
aws, machine-learning, mlops, real-time-ml, streaming-analytics, anomaly-detection, sagemaker, kinesis, eventbridge, cloudwatch, python, portfolio-project