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Azure ML & Cloud
Docker + Kubernetes
Containerized AI workers — portable, scalable, auditable
What it is
Docker for reproducible builds; Kubernetes for orchestration. Every Vaaani service ships as a container so it runs identically on the customer's cloud, on-prem, or my laptop.
How Vaaani uses it
- Standardized deployment across AWS, Azure, GCP
- Zero-downtime rollouts with health checks
- Horizontal autoscaling based on queue depth or CPU
- Secret management, config maps, namespaces for multi-tenancy
Why it makes the cut
Containers eliminate 'works on my machine.' K8s eliminates 'who restarts it at 3am?' Both are non-negotiable for production AI.
Sample code
# Dockerfile FROM python:3.12-slim COPY . /app RUN pip install -r /app/requirements.txt CMD ["uvicorn", "app:api", "--host", "0.0.0.0"]
Related in the Vaaani stack
Have a project that needs Docker?
30-min discovery call. You describe the busywork; I map it to an AI worker and a budget.