Solution

Quantiphi team assessed the current state of data architecture, data science maturity, and business priorities, and developed customer curated architecture along with deployment and demonstration of ML Ops pipeline for the shortlisted Churn Prediction use case

The engagement was planned in two phases, each including two sprints – the discovery phase followed by the pilot implementation phase

The discovery phase consisted of conducting sessions with the client to understand the use case and the data- schema, columns, and tables involved.
Post the discovery session, the team prepared a detailed document on the current state assessment and the high-level solution architecture that can be used for implementation.
The pilot phase included architecting, modeling, inference, and model serving along with a demonstration of model monitoring and CI/CD frameworks.

Results

• Developed robust orchestration pipelines
• Automated pipeline to handle KPIs and score tuning
• Created ML Ops framework to address customer churn use case