RetailMind
Graph-based product recommendation experiment with a reproducible delivery pipeline.
- Type
- AI / ML
- Client
- Independent ML research prototype
- Role
- ML engineering · MLOps
- Year
- 2024
Evidence / status
Prototype. Reported outcomes await verification.
Challenge
Recommendation infrastructure without an ML team
The work explored whether a smaller retail dataset could support graph-based recommendations and a repeatable path from training to API serving.
Constraints
- Experiments needed reproducible data and parameters.
- Model comparison needed more structure than notebook output.
- The serving interface had to remain simple to integrate.
- Retraining needed a documented path rather than manual steps.
Approach
Key decisions
Graph-based collaborative signals
LightGCN was selected to model relationships between customers and purchased products.
Versioned data pipeline
DVC records dataset and preprocessing versions alongside code changes.
Tracked model experiments
MLflow captures model parameters and ranking metrics for comparison.
Small API serving surface
FastAPI exposes recommendation requests without coupling clients to the training stack.
Evidence
What can be stated
Current status: Prototype. Unverified outcomes are labelled instead of presented as proof.
Model quality, dataset size, and latency figures remain unverified portfolio claims.
Needs verification
Technology stack
- PyTorch
- FastAPI
- DVC
- MLflow
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