PricePulse is an end-to-end production MLOps platform built on AWS that automates the complete machine learning lifecycle — from data ingestion to model deployment — enabling real-time price predictions through a fully serverless architecture.
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.
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.
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.
PricePulse turns raw data into live, reliable predictions with virtually no manual operational overhead.
Engineering Production-Grade ML Automation PricePulse was built to replicate the ML infrastructure patterns used at scale in industry, end to end.
“The best MLOps platforms are the ones you stop thinking about — they just keep working.” — Manu Sharma, CEO
Blending Automation with Production Safety Every layer of PricePulse was designed to remove manual bottlenecks without sacrificing control.
Delivering Production ML Without the Overhead PricePulse shows how full automation and cost efficiency can coexist in a live ML system.
