Why Feature Stores Matter for Production ML
Feature stores solve one of the most underrated problems in ML — the gap between training and serving. Here's why every ML team should care.
Blog
Thoughts on MLOps, infrastructure, and building reliable AI systems.
A Practical Guide to Kubernetes for ML Workloads
Kubernetes isn't just for web services. Here's how to configure it for GPU-heavy ML training and inference workloads without losing your mind.
Building CI/CD Pipelines for ML Models
Traditional CI/CD doesn't work for ML. Here's how to build pipelines that validate data, test models, and deploy safely — with rollback.