Software engineering, ML systems, and developer tooling
I write about software engineering. Find my articles here and across leading tech publications. Expect concrete notes on Docker, AI infrastructure, performance tuning, and the system work behind side projects that need to operate like real products.
At a glance
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ML • Docker
Have you ever built a brilliant time-series model, one that could forecast sales or predict stock prices, only to watch it fail in the real world? Well, this is a common frustration. Your model works perfectly on your machine, but the moment you deploy it in a Docker container, it seems to develop amnesia. It forgets everything it knew yesterday, making its predictions for tomorrow useless.
ML • Docker
Multi-stage Docker builds separate build-time dependencies from runtime requirements, dramatically reducing production image sizes.
ML • Docker
Use -slim base images, multi-stage builds, smart layer caching, and chained RUN commands to build lean, fast, and production-ready Docker images.
I joined the Beginner's Guide to AI podcast to break down how to build scalable AI agents — covering architecture patterns, orchestration strategies, and practical lessons from production deployments.
I spoke with The Digital Executive about scaling agentic AI systems — discussing the challenges of building autonomous AI agents that operate reliably at enterprise scale.
I spoke with Ashutosh Garg on The Brand Called You about platform leverage, ethics, and democratizing AI development — covering open-source versus proprietary AI model economics and the future of agentic systems.
A simple demonstration of time series prediction using ML model and redis db hosted in Docker container.
A containerized solution for hosting transformer models using Flask, Gunicorn, and Docker with AWS SageMaker deployment support. Build once, run anywhere!
A evolving codebase that demonstrates various techniques to optimize docker image for size and performance.