AI Engineering Lab: Projects
Portfolio projects that span multiple weeks. The program's weekly use cases build toward these; each project has an inspectable artifact with a number attached, that is the Zorost gate: a stranger can use it, and you can show them what it did.
The capstone: ZoroLogistics Lakehouse Intelligence (Weeks 21 to 24)
The graduation project, deployed on Databricks:
- Data: medallion lakehouse (bronze → silver → gold) from the Week 1 dataset
- Pipelines: streaming shipment events with quality expectations (Week 22)
- ML: point-in-time ETA model registered in MLflow, served behind the AI Gateway (Week 23)
- GenAI: policy RAG via Vector Search + a Genie space for ops analysts (Week 23)
- Production: deployed end-to-end as a Databricks Asset Bundle with CI/CD, row/column governance, and a FinOps dashboard (Week 24)
See reference/platforms/databricks/capstone/ (added in Week 21) and curriculum/week-24/.
Milestone projects by phase
| Project | Built in | Artifact |
|---|---|---|
| ZoroLogistics data generator + silver dataset | Weeks 1 to 2 | Seeded generator + validated CSV/Parquet |
| ETA prediction (ML → DL) with model cards | Weeks 3 to 4 | Model card v2 with error analysis |
| BoL extraction suite + RAG policy bot | Weeks 6 to 7 | Prompt suite + eval scores |
| Local triage model (quantized, fine-tuned, served) | Weeks 8 to 10 | Quality-vs-cost report |
| ZoroEval, the eval harness | Week 11 | CI-gated eval script |
| ETA CLI + Support Bot MVP via coding agents | Weeks 12 to 13 | Deployed CLI + loop log |
| Support triage multi-agent team over MCP | Weeks 14 to 16 | A/B report vs single agent |
| ZoroLab personal assistant (OpenClaw + Hermes-class model) | Week 17 | Skills + ops runbook |
| The same support agent on Azure / Google / AWS | Weeks 18 to 20 | Three-cloud comparison matrix |
How to make projects count
- Give every project a public URL (GitHub repo, deployed endpoint, published dashboard), an artifact someone else can inspect.
- Attach numbers: eval scores, cost per task, p95 latency, recall@k.
- Write the limitation honestly, what the artifact cannot do, and what you would do next. That sentence is worth more than the demo.
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