From ML Proof of Concept to Production on AWS
Most ML projects never make it past the notebook. I help teams build production-grade ML infrastructure that scales, monitors itself, and doesn't burn through your budget.
70%
Reduction in inference costs
10x
Faster model deployment
18x
AWS & Scrum certified
The Problem
Teams struggle to move ML models from experimentation to production reliably.
SageMaker endpoints idle 23 hours a day, burning budget on unused inference capacity
No CI/CD pipeline for models — retraining and deployment is manual and error-prone
Model drift goes undetected for weeks, silently degrading prediction quality
Training jobs run on expensive on-demand instances instead of spot
No feature store — feature engineering is duplicated across teams
My Approach
ML Infrastructure Audit
I review your current ML stack, identify bottlenecks, and map out the path from POC to production-grade infrastructure.
Pipeline Design & Implementation
SageMaker Pipelines for automated training, validation, and deployment. Model Registry for version control. Feature Store for shared features.
Cost-Optimized Inference
Serverless inference endpoints, auto-scaling policies, and multi-model endpoints to eliminate idle capacity.
Monitoring & MLOps
Model quality monitoring, data drift detection, automated retraining triggers, and alerting — so you know when models degrade.
Related Case Study
ML Pipeline for Real-Time Fraud Detection
Challenge
A fintech startup needed to move their fraud detection model from a Jupyter notebook to a real-time inference pipeline processing 10K+ transactions per minute.
Solution
Designed a SageMaker Pipeline with automated retraining, a Feature Store for real-time features, and a multi-model endpoint with auto-scaling.
Results
Model deployment time reduced from 2 weeks to 4 hours. Inference costs cut by 65% through serverless endpoints. False positive rate improved 23% with automated retraining.
What You Get
- Architecture design
- pipeline implementation
- model deployment strategy
- monitoring setup
- cost analysis
ML Infrastructure Review
From $1,999
Comprehensive review of your ML stack with actionable optimization roadmap
Ready to Get Started?
Engagement Options