Thinking Out Loud, From the People Building It.
A running archive of how we think about models, data, and deployment — written for technical and business readers alike.
MLOPS
From Notebook to Production: A Practical MLOps Playbook
Most ML models die in notebooks. Here's the architecture, tooling, and team culture that gets them to production — and keeps them running.
Large Language Model
RAG vs Fine-Tuning: Choosing the Right LLM Strategy
When to build a retrieval pipeline, when to fine-tune, and when to combine both. A practical decision framework for enterprise AI teams.
Data Engineering
Why Your Data Pipeline Fails at 10× Scale — and the Fix
Schema drift, quality degradation, and processing bottlenecks. The three failure modes we see most often, and the patterns that actually solve them.
Clinical NLP
Structuring 40,000 Monthly Unstructured Clinical Notes with Transformer Pipelines
How fine-tuned biomedical language models extract ICD-10 codes, prescriptions, and patient trajectories with 99.1% precision in HIPAA environments.
Pathology AI
Edge Computer Vision in Whole-Slide Histopathology Screening
Achieving sub-second tile classification across gigapixel digital pathology slides while maintaining zero false negatives for high-grade dysplasia.
Graph Neural Nets
Real-Time Fraud Ring Detection with Graph Neural Networks and Sub-50ms Latency
Uncovering multi-hop synthetic identity rings and laundering patterns across millions of daily streaming transactions without adding customer friction.
Document AI
Zero-Shot Document Extraction: Moving Past Rigid OCR Templates
Why traditional OCR fails on passports, tax filings, and bank statements — and how multimodal document models eliminate manual review queues.
Vector Search
Multi-Modal Visual Search Engines Driving 28% Higher Average Order Value
Building billion-vector approximate nearest neighbor indexing systems for instant screenshot and photo-matching product discovery.
Demand Forecast
Probabilistic Demand Forecasting for 80,000+ Fast-Moving Retail SKUs
Combining hierarchical Bayesian modeling with DeepAR to eliminate inventory stockouts and avoid millions in tied-up working capital.
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