Problem
Turn a large ratings dataset into useful movie recommendations, then carry the work beyond a notebook into a service that can be tested, packaged and observed.
01 / Technical case study
Netflix
Recommendation
Engine
Recommender systems · Machine learning · MLOps
Production-oriented movie recommendation engine combining recommendation strategies with API serving, experiment tracking, containerization, testing and monitoring.
From ratings
to recommendations.
01Data
02Processing
03SVD / popularity
04Recommendation layer
05Inference
06FastAPI
07Docker
08Monitoring
Data
The public repository documents work on a dataset of more than 18 million ratings. Processing prepares user, item and rating signals for repeatable experimentation.
Architecture
A deliberate sequence connects data preparation, recommendation models, an inference layer, API serving, containerization and monitoring.
Models
The verified implementation combines SVD collaborative filtering with a popularity baseline. The system is structured so recommendation strategies can be compared rather than treated as a single opaque model.
Inference
A dedicated inference layer separates recommendation logic from the delivery surface, making model behavior easier to test and integrate.
Engineering
FastAPI exposes the recommendation workflow as software. Automated tests and clear module boundaries support changes without turning the project into a one-off demo.
MLOps
MLflow tracks experiments, Docker packages the service, and automated CI checks the project as it evolves. Monitoring completes the path from model work to an operable system.
Results
No accuracy, latency or traffic claims are published here. The evidence is the engineering surface: reproducible experiments, serving, packaging, tests and monitoring.
Lessons
A recommender is more than an algorithm. Baselines, evaluation, serving contracts and operational feedback are part of the model’s real behavior.
Source
Inspect the public repository for the current implementation, documentation and commit history.
Open repository