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
    • Merge demographics, customer, geo-location, credit-model, clickstream & marketing campaign data to create a single source of truth
    • Use model management on GCP to track and maintain 150+ ML models


    • Auto-scalable infrastructure on GCP to manage variable workloads thus reducing cost
    • Big-query and data-proc used in tandem to provide compute-horsepower on a case to case basis based on cost
    • Leveraging Kubeflow and Kubernetes for model management and deploying model endpoints for down-stream consumption


    • The UDP since its inception have been processing 250 Tb’s of data weekly
    • Overall reduction in turn-around time by 70 percent for computationally heavy jobs
    • 30-35 per-cent overall cost savings

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