AI & Data
MLOps Platform & Recommendation Engine
Built an end-to-end MLOps platform and deployed a personalised product recommendation engine that increased average order value by 22% within 60 days of launch.
The Challenge
The data science team had built several strong recommendation models in notebooks — but had no infrastructure to deploy them reliably, monitor their performance, or retrain them automatically as product catalogues changed.
Our Solution
We built a full MLOps platform on Kubeflow with Feast as the feature store, automated retraining pipelines triggered by data drift detection, and a shadow deployment framework for A/B testing models before full rollout.
How We Did It
- MLOps maturity assessment — identified the 3 highest-value deployment candidates
- Kubeflow Pipelines for orchestrated training, evaluation, and deployment workflows
- Feast feature store with point-in-time correct feature retrieval for training
- Evidently AI for automated data drift monitoring with Slack alerting
- Shadow mode A/B testing: new models serve predictions silently before promotion
Key Outcomes
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