Two weeks in, you have all the pieces: Git and DVC for versioning, MLflow for tracking, and Docker for reproducible environments. A single docker compose up that spins up a tracking server, runs a versioned experiment, and logs every result.
Stop running MLflow and your training script in separate terminals. Today you'll wire them together with Docker Compose , one command, a full local ML platform, ready to log every experiment automatically.
Multi-stage builds, .dockerignore hygiene, layer caching, and dependency pinning , the engineering discipline that separates a "it works" image from one you'd actually ship to production.
Self-hosting LLMs Local Machines and on EC2/GKE with Ollama — when open-source beats API services (Day 15)
How to Deploy Identity-Based, Zero-Trust Secure Overlays Across EC2, Docker, and Kubernetes
30 Days of MLOps Challenge: Day 11
Example: Kubernetes, Terraform, Docker, AWS, MLOps...
