MODULE 2: CORE ARCHITECTURE & COMPONENTS /// DOCKER COMPOSE FOR ML APPS

Orchestrating ML Apps

Stop deploying naked models. Learn to orchestrate an API, Cache, and Model Server into a robust, multi-container system using Docker Compose.

docker-compose.yml
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AIDE:Machine Learning models rarely run in isolation. They need an API, maybe a database, and a model server. Docker Compose orchestrates them.

Architecture Map

UNLOCK NODES BY DEPLOYING SERVICES.

Services

A service is a computing instance (container) in your distributed application.

System Diagnostics

What does a 'service' block define in docker-compose?

Deployment Challenges

Deployment Badges

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Orchestrator

Successfully structure a multi-container Docker Compose file.

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Network Ninja

Map ports correctly to expose an ML API to the host.

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Volume Virtuoso

Mount a volume to persist a loaded ML model outside the container.

Why Docker Compose in MLOps?

In modern Machine Learning Operations (MLOps), deploying a model script is rarely enough. A production-ready inference system typically consists of an API gateway (FastAPI/Flask), a dedicated model server (TensorFlow Serving or TorchServe), and auxiliary services like Redis for caching or Celery for async batch predictions.

The "Heavy Model" Problem

Unlike standard web apps, ML images can easily swell to 5-10GB if you bake the model weights directly into the Dockerfile. Docker Compose solves this by allowing strict `volumes` bindings. You keep your Docker image small containing only the runtime dependencies, and mount the model artifacts from the host system directly into the container at runtime.

❓ MLOps Compose FAQ

Why not just use Kubernetes?

Kubernetes is the gold standard for large-scale orchestration, but it comes with immense cognitive load. Docker Compose is the perfect intermediate step. It allows developers to test the multi-container architecture locally before writing complex Helm charts or K8s manifests.

How do containers communicate in Compose?

Docker Compose provisions a default bridge network. If you name a service `ml_model_server` in your YAML file, your FastAPI service can simply send HTTP/gRPC requests directly to `http://ml_model_server:8500`. No hardcoded IP addresses needed.