AI & MLOps
ML-based recommendation prototype for weekly decisions
ML-based recommendation prototype using Python services, API-driven data flows, player comparison, and recommendation workflows.
Tech stack
Overview
ML-based recommendation prototype using Python services, API-driven data flows, player comparison, and recommendation workflows.
Problem
Fantasy football users needed explainable weekly lineup recommendations from changing player statistics and fixture conditions.
Impact
Architecture
Step-by-step implementation path used to deliver this project.
Step 1
Python data ingestion services
Step 2
Feature engineering and player scoring
Step 3
Recommendation API layer (Flask/FastAPI)
Step 4
Evaluation and monitoring with MLflow, Elasticsearch, and Kibana
Features
Built advisory recommendation flows with model experiments and evaluation tracking.
Implemented API-driven data ingestion for structured feature generation.
Added monitoring views for trend inspection and decision-support iteration.
Media
Limitations
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