Hdmovies20 |verified| -

| Area | Details | |------|----------| | | Backend: Python (FastAPI) or Node.js (NestJS). Frontend: React (Next.js) or Vue (Nuxt) – component‑driven. | | Data Store | Clickstream → Kafka → S3 → Spark. User profiles & features → PostgreSQL + Redis cache. | | Model Serving | TensorFlow‑Serving / TorchServe or a simple Flask API behind an Nginx load‑balancer. | | Scalability | Horizontal scaling of scoring service; 95th‑percentile latency ≤ 30 ms. | | Privacy | Anonymize IP, respect GDPR – store consent flag; allow “opt‑out of personalization”. | | Testing | Unit tests for scoring logic, integration tests for end‑to‑end recommendation flow, load‑test (k6) for 10 k RPS. | | Monitoring | Prometheus metrics: request latency, error rate, cache hit‑ratio. Grafana dashboards + alerts. | | Deployment | Docker containers, Helm chart on Kubernetes (auto‑scaling). CI/CD pipeline with feature‑flag gating. |

| Week | Milestone | |------|-----------| | | Requirements finalization, data schema design, create feature‑flag infrastructure. | | 2‑3 | Build data pipeline (Kafka → Spark) & feature store; ingest first 30 days of interaction data. | | 4 | Train baseline collaborative + content models; create scoring micro‑service; unit tests. | | 5 | Front‑end component library for recommendation rows (React/Vue). | | 6 | Integrate API with UI; add feedback UI, explainability overlay. | | 7 | A/B test rollout (50 % users), monitor metrics, gather early feedback. | | 8 | Iterate on model (add temporal decay, fine‑tune hyper‑params), finalize rollout to 100 % and hand‑off docs. | hdmovies20