Powering Predictions with Serverless Precision

• The Challenge The core problem was moving beyond notebook-based ML experimentation to a production-grade system where models are automatically trained, evaluated, versioned, and deployed without manual intervention — while ensuring reliability through continuous monitoring.
• Complexity and Innovation We built a fully automated SageMaker Pipeline handling data ingestion, training, evaluation, and registration with zero manual steps, backed by a Model Registry with a human approval workflow for production safety. A custom FastAPI inference server was containerized in Docker, pushed to ECR, and served via a SageMaker Serverless Endpoint — eliminating idle compute costs entirely. CloudWatch alarms integrated with SNS deliver real-time error alerting.
• The Process We separated training, serving, and monitoring concerns from the start — building SageMaker-compatible training scripts, baking preprocessing logic directly into the serving layer, and automating the full workflow through SageMaker Pipelines with per-run metric tracking.
• Automated, Scalable Price Intelligence PricePulse turns raw data into live, reliable predictions with virtually no manual operational overhead.

Feature Inventory

Engineering Production-Grade ML Automation PricePulse was built to replicate the ML infrastructure patterns used at scale in industry, end to end.

• Automated SageMaker Pipeline: data ingestion, training, evaluation, and registration in one seamless flow
• Multi-artifact model saving (model + encoder + scaler) for consistent inference
• Custom Docker container with FastAPI serving on ECR
• Serverless inference endpoint with zero idle compute cost
• Public REST API via Lambda + API Gateway (no AWS credentials required)
• Model Registry with versioning and human approval workflow