Price Prediction API

Using ML for automated price estimation

Project context: External blogpost

Personal contributions:

  • Led development of 3 backend APIs serving ML models for real estate price prediction, leading to extended client engagement with 2 more APIs, by following MLOps best practices and CI/CD with Kubernetes;
  • Owned the development of 5 ML models for real estate pricing, while also serving as primary on-call for the 5 APIs on Kubernetes CI/CD, operating across different markets for an international client;
  • Reduced recurring GCP storage costs by optimizing ML output file size by an order of 10;
  • Reduced latency per ML API call by 3s, by caching data for queries on GCP BigQuery;
  • Configured access for team members with GCP IAM and K8s RBAC;
  • Handled data ingestion, process and monitoring data pipelines for weekly model retraining;