Building measurable, scalable & responsible ML

Proven ML systems that work in production - with performance metrics, failure analysis, and responsive interfaces.

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Quantified Impact

Hard numbers behind the implementations

Model Performance

  • Average MAE Reduction 23%
  • Training Speedup 4.2x

System Metrics

  • P99 Latency 47ms
  • Throughput 1,240 req/s

Data Quality

  • Missing Values <3%
  • Class Balance 1:4 ratio

Production-Ready Projects

Measured impact through deployment metrics and failure analysis

Multimodal Price Intelligence

Improved pricing accuracy by 23% (MAE) on multi-domain e-commerce data

PyTorch SHAP Streamlit MAE: 0.23
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Federated Demand Forecasting

Reduced data leakage by 89% while maintaining 95% of centralized model accuracy

TensorFlow Federated Learning Docker Accuracy: 95%
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Anomaly Detection

Detected 98% of failures with <1% false positive rate

PyTorch TSFresh Gradio Recall: 0.98
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