"But it works on my machine!"

If I had a rupee for every time I heard this during model deployment, I'd retire.

Docker solved this problem for software engineering a decade ago. Yet I still see ML engineers in 2026 shipping models as loose Python scripts with a requirements.txt that hasn't been updated in months.

Here's my standard Docker pattern for ML: start with python:3.11-slim (not the full image — you don't need gcc in production). Install dependencies. Copy model artifacts. Expose a FastAPI endpoint. Add a health check. Done.

Multi-stage builds cut your image size by 60%. GPU support with nvidia-docker works out of the box. Docker Compose handles the cases where your model needs a vector database and a cache running alongside it.

Containerization is genuinely table stakes now. Not knowing Docker as an ML engineer in 2026 is like not knowing Git in 2015.

The good news: it's a weekend of learning that pays dividends for years.