Problem
A US-based enterprise client needed a cloud-native ETL and ML platform on AWS — ingest raw data from multiple operational sources, transform it into model-ready features, train and deploy production models on SageMaker, and serve predictions back to downstream consumers with proper observability.
Approach
End-to-end AWS data + ML stack. ETL pipelines using AWS-native services to ingest from multiple sources; feature engineering and store; SageMaker training jobs with experiment tracking; SageMaker endpoint deployment for real-time inference; CloudWatch observability and alerting; IAM and VPC isolation appropriate for the enterprise security posture.
Stack
AWS SageMaker · S3 · Glue / Lambda · CloudWatch · IAM / VPC · Python · scikit-learn / XGBoost
Outcome
Production ML platform serving predictions reliably from a stack the client's internal team could maintain after handover. Engagement validated end-to-end ownership of an enterprise AWS ML deployment — from data ingestion through model deployment to operations.