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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.
Multi-stage Docker builds separate build-time dependencies from runtime requirements, dramatically reducing production image sizes.
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.