The approach to address the client’s challenge included:

  • Building re-usable data pipelines to move away from the current Hadoop-based system to GCP
  • Leveraging the GCP environment to build a cost-optimized ecosystem to cater to both the business users and the data-scientist community
  • Merging demographics, customer, geo-location, credit-model, clickstream & marketing campaign data to create a single source of truth

Using a model management on GCP to track and maintain 150+ ML models


Our solution helped the client:

  • Develop an auto-scalable infrastructure on GCP to manage variable workloads thus reducing cost
  • Use big-query and data-proc in tandem to provide compute-horsepower on a case-to-case basis based on cost
  • Leverage Kubeflow and Kubernetes for model management and deploying model endpoints for down-stream consumption


  • The UDP since its inception has been processing 250 TBs (Terabytes) of data weekly
  • Overall reduction of processing time in computation-intensive jobs by 70 percent
  • Overall costs reduced by 30-35 percent