System Design for Beginners
The system design fundamentals I wish someone explained to me when I was starting out — no fluff, just the mental models that actually matter.
System Design • Beginners
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Prometheus on OpenShift for Production ML Monitoring
The monitoring setup I couldn't find a good guide for — Prometheus + Thanos on OpenShift for ML inference workloads. Metrics collection, reliability tracking, and observability from scratch.
Prometheus • OpenShift • MLOps • Monitoring
Read article →MLOPS Architecture
How I approach production ML inference on Kubernetes — model storage decisions, OCI-baked deployments, zero-downtime rollouts, and the Helm chart patterns that make life easier for app teams.
MLOPS • architecture • MLOps • life-cyle-deployment
Read article →Model Drift — A Complete Guide
Everything I've learned about model drift after watching production models quietly degrade — detection methods, the stats behind them, and knowing when to retrain vs. when to wait.
MLOps • Model Drift • Monitoring • Production ML
Read article →AI Agents — What They Are and How to Use Them
What I learned after a year of building with AI agents — how they actually differ from chatbots, the patterns that work, and where I use them in my own workflow.
AI • Agents • LLM • Fundamentals
Read article →How Big Companies Do Drift Monitoring
I dug through engineering blogs from Uber, Netflix, LinkedIn, Airbnb, Meta, and Google to figure out how they actually handle drift. Six companies, six architectures — all sourced.
MLOps • Drift Monitoring • Industry Research • Production ML
Read article →LLM Fundamentals — Deep Dive
My working notes on how LLMs actually work — from attention mechanics and tokenization to RLHF, LoRA, quantisation, and scaling laws. Written while trying to explain these things precisely.
LLM • Transformers • Deep Learning • AI Fundamentals
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