How to design a scalable MLOps pipeline for real-time recommendation engines in e-commerce?

• Use a unified feature store (online + offline) to decouple feature engineering from model training and enable low‑latency feature retrieval for real‑time scoring [1].
• Implement CI/CD pipelines that automatically trigger model retraining on new interaction data, run automated validation tests, and deploy to a container‑orchestrated serving layer (e.g., Kubernetes) with blue‑green or canary releases [2].
• Leverage model‑serving frameworks that support low‑latency inference (e.g., TensorFlow Serving, Triton) and horizontal autoscaling to handle traffic spikes during peak shopping periods [3].
• Set up continuous monitoring of data drift, prediction latency, and business metrics (CTR, conversion) with automated alerts and rollback mechanisms to maintain model relevance and system reliability [4].
• Conduct A/B/n experiments at the serving layer to compare candidate models against the production baseline before full rollout, ensuring that improvements translate to measurable e‑commerce KPIs [1][3].

Bottom line: A scalable real‑time recommendation MLOps pipeline combines a feature store, automated CI/CD, low‑latency autoscaled serving, continuous monitoring, and rigorous experimentation to keep models fresh, reliable, and aligned with e‑commerce business goals.

Sources

1. Building an E-commerce Recommendation System with MLOps
2. A Real-World End-to-End MLOps Pipeline for Large-Scale E-Commerce Product Recommendations | by Fourat OUESLATI | Medium
3. End-to-End MLOps for Recommender Systems | Qwak
4. E-Commerce Recommendation System Report | PDF | Apache Spark | Computing