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Cloud Analytics Architect Resume



As Is Scenario Assessment

Requirements Gathering

Data Science Model Life Cycle

Cloud based Solution Architecture

Docker Containers, Kubernetes & ML Pipelienes

Model Deployment & Monitoring in Cloud (MLOps)(Azure ML Service/ AWS Sage Maker/ Google AI Platform, IBM)

Actual Project Experience in at least one Cloud based ML environment

Azure Cloud Offerings

Azure ML Service & Python SDK

Python 3.x Programming

RESTful APIs for wrapping ML Models / System Integration

CI/CD Integration


Cloud Analytics Architect

Confidential, Houston


  • Machine Learning (ML) Model Deployment, Support & Monitoring:
  • Assess and document current Data Science Platform Landscape during discovery phase
  • Gather and assess the data science model deployment and refresh requirements for two applications deployed in production - e.g., data sources, models & algorithms, modeling codes/ data pipeline, performance metrics, interfaces, library, version dependencies etc.
  • Define Model Deployment and Monitoring Solution Architecture
  • Develop industrialized Model Deployment & Monitoring Plan forAI ML Models

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