Era Context & Historical Background
To solve complex supply chain volatility, Rohit combined sequence-aware PyTorch Recurrent Neural Networks (LSTM) with tabular XGBoost models to predict multi-sku inventory re-order points.
Key Technical Breakthroughs & Architecture
Hybrid Neural-Boosting Model
Fused sequential LSTM trend embeddings with static XGBoost promotional features for high-precision demand prediction.
Automated Re-order Forecasting
Generated 90-day inventory re-order forecasts across 15,000+ SKUs with 94.2% historical accuracy.
MLflow Experiment Tracking
Implemented MLflow tracking pipelines to monitor model drift and automate hyperparameter tuning runs.
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Author & Architect
Rohit
AI & Data Science Consultant
2+ Decades AI ExperienceFirst project in AI & ANN in 2004 at IIT Roorkee under the mentorship of Dr. Sunil Padhi (HOD, Electrical Department), writing neural network backpropagation in C language to predict solar sunspots. Today designing stateful Agentic AI networks at rcode.in.
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