ML Training Pipeline & MLOps
E-commerce Retailer
The Problem
E-commerce retailer losing $50k monthly in spoilage and stockouts. No demand forecasting capability—ordering decisions based on intuition. Lacked infrastructure for ML model development, training, versioning, or deployment. Data scattered across multiple systems with no centralized pipeline. Needed end-to-end solution from raw data to production predictions.
What We Built
Built complete MLOps infrastructure from scratch. Created automated data pipeline consolidating sales history, inventory levels, seasonality data, and external factors (weather, holidays). Developed custom PyTorch demand forecasting model with LSTM architecture. Implemented MLflow for experiment tracking, model versioning, and artifact management. Built FastAPI inference service with automated A/B testing framework. Created monitoring dashboard tracking model performance, drift detection, and business KPIs. Implemented automated retraining triggers based on drift metrics. Integrated seamlessly with existing POS and inventory management systems. Deployed on Kubernetes with auto-scaling and zero-downtime updates.
Tech Stack
Results
- ✓Waste reduction: $50k monthly → $22k monthly (56% decrease)
- ✓Stockouts reduced by 67%, improving customer satisfaction
- ✓Model accuracy: 94% for 2-week demand forecasting
- ✓Automated retraining every 2 weeks with drift detection
- ✓Full MLOps pipeline: data ingestion → training → deployment → monitoring
- ✓ROI achieved in 2 months
Client Feedback
"Needed someone to clean up our ML pipeline mess. They standardized it, added monitoring, and trained our team. Waste cut by 56%, paid for itself in 2 months."
— Head of Operations, E-commerce Retailer