Technical Articles
Deep dives into problems I've solved, mistakes I've made, and lessons learned along the way. No fluff, just honest technical insights from building real systems.
docs: scaling data pipelines for real-time analytics
A deep dive into building scalable data infrastructure that can handle millions of events per second. Real architectural patterns, tools, and best practices for modern data engineering. What actually works when Stack Overflow answers aren't enough.
feat: deploying LLMs in production - lessons learned
Practical insights from deploying large language models in production environments. The optimization techniques that actually matter, monitoring strategies that work, and cost management tricks that will save your budget.
refactor: attribution modeling from a data engineer's perspective
How to build robust attribution models that actually measure marketing impact across multiple channels. Technical deep-dive into data processing, model architecture, and validation techniques that go beyond basic last-click attribution.
perf: computer vision at the edge - optimization strategies
Techniques for deploying computer vision models on edge devices with limited computational resources. Model compression, quantization, and hardware acceleration strategies that actually work in the real world.
feat: time series forecasting for energy systems
Advanced techniques for forecasting electrical demand using deep learning. Exploring LSTM networks, attention mechanisms, and ensemble methods for improved prediction accuracy. Real case studies from energy grid optimization.
docs: MLOps best practices - from notebook to production
A comprehensive guide to building robust MLOps pipelines that ensure reliable model deployment and monitoring. Best practices for version control, testing, and continuous integration that actually scale.
build: automated data quality monitoring at scale
Building automated systems for monitoring data quality in large-scale data pipelines. Techniques for anomaly detection, data profiling, and alerting mechanisms that catch problems before they break everything downstream.
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