Data & ML Engineering · 300-level
Data & Machine Learning Engineering
A staff-level tour of the data and ML engineering stack — warehouse modeling, batch and streaming pipelines, data quality, feature stores, training and evaluation, deployment, drift monitoring, and production RAG — taught through mechanisms, real tools, and the dollar-and-latency consequence of getting each one wrong.
DML-3013 creditselectiveno prerequisites
What's inside
Sections & lessons
01
Data modeling & warehouses
- OLTP vs OLAP: why analytics doesn't belong on your production databaseconcept40 min
- Dimensional modeling: star vs snowflake and the discipline of grainconcept40 min
- Columnar storage and partitioning: how the warehouse scans less to cost lessconcept35 min
- Slowly-changing dimensions: keeping history without corrupting the presentconcept35 min
02
Batch & streaming pipelines
- ETL vs ELT: where the transform runs and who pays for itconcept40 min
- Spark at scale: partitions, shuffles, and the skew that stalls a jobconcept40 min
- Streaming with Kafka: partitions, ordering, and delivery semanticsconcept35 min
- Orchestration and idempotent backfills: Airflow, Dagster, and replay-safetyconcept35 min
03
Data quality & governance
- Data contracts: schemas as enforceable agreementsconcept40 min
- Testing data: dbt tests and Great Expectationsdemo40 min
- Lineage and cataloging: knowing what breaks three hops downstreamconcept35 min
- PII handling and governance: classification, masking, and accessconcept35 min
04
Feature engineering & feature stores
- Data leakage: the bug that inflates every offline metricconcept40 min
- Train/serve skew: when production features drift from trainingconcept40 min
- Feature stores: offline/online parity as an architectureconcept35 min
- Point-in-time correctness: as-of joins that don't leak the futureconcept35 min
05
Model training & evaluation
- Splitting data honestly: temporal and grouped splitsconcept40 min
- Choosing the metric that matches the decisionconcept40 min
- Overfitting, baselines, and cross-validationconcept35 min
- Imbalanced data: when accuracy liesconcept35 min
06
MLOps & deployment
- Serving patterns: batch, online, and streaming inferenceconcept40 min
- Versioning and reproducibility: pinning model, data, and code togetherconcept40 min
- CI/CD for ML: testing a model before it shipsconcept35 min
- Shadow and canary deploys: validating in production without betting the businessdemo40 min
07
Monitoring & drift
- Data drift vs concept drift: two failures that look alike on a dashboardconcept40 min
- Monitoring models when labels arrive lateconcept35 min
- Retraining triggers: deciding when to retrain and proving it helpedconcept35 min
08
LLMs & RAG in production
- Embeddings and vector databases: retrieval as nearest-neighbor searchconcept40 min
- Retrieval quality and RAG evals: measuring what the retriever actually returnsconcept40 min
- Guardrails, cost, and latency in production RAGconcept35 min
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