CMU’s MCDS blends distributed systems, ML engineering, and responsible AI. Students optimize petabyte-scale ETL on Spark, deploy multimodal transformers in Kubernetes, and implement privacy mechanisms under GDPR constraints. A capstone with partners like Netflix or NOAA tasks teams with end-to-end productionization of data products—from ingestion to dashboard storytelling.
Delta-lake architecture mirroring CDC change-data-capture for fintech auditability
Vector-search service powering real-time semantic recommendations
Auto-scaling feature-store with online/offline consistency guarantees
Differential-privacy wrapper for mobility data in smart-city APIs
Streaming anomaly detection on IoT sensor telemetry using Apache Flink
Prompt-engineering dashboard tracking LLM hallucination metrics
Hybrid lakehouse/warehouse cost-optimization simulator
Data-centric AI pipeline cleaning satellite imagery for flood mapping
ML fairness auditor automatically generating Shapley-based reports
Synthetic-data generator benchmarking fraud-detection models
Explainable ranking algorithm for talent-matching platforms
Graph ETL framework ingesting multi-modal cybersecurity alerts
Green-compute scheduler minimizing carbon intensity of GPU jobs
AutoML workflow integrating Bayesian hyper-tuning and lineage tracking
Storytelling notebook converting complex analyses into press-ready articles
Engineer robust, ethical data solutions at CMU MCDS.
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