Container registries for ML in MLOps - Commands & Configuration

Introduction

When you build machine learning models, you often package them with their code and environment to run anywhere. Container registries store these packages so you can share and reuse them easily without setup problems.

When you want to share a trained ML model with your team without sending large files.

When you need to deploy your ML model in different environments like testing and production.

When you want to keep track of different versions of your ML model containers.

When you want to automate ML model deployment in a CI/CD pipeline.

When you want to run your ML model on cloud services that pull containers from a registry.

Commands

This command builds a Docker container image named 'my-ml-model' with tag '1.0' from the current folder. It packages your ML model and environment together.

Terminal

docker build -t my-ml-model:1.0 .

Expected Output

Sending build context to Docker daemon  12.34MB

Step 1/5 : FROM python:3.9-slim

 ---> 123abc456def

Step 2/5 : COPY . /app

 ---> Using cache

Step 3/5 : RUN pip install -r /app/requirements.txt

 ---> Using cache

Step 4/5 : CMD ["python", "/app/predict.py"]

 ---> Using cache

Step 5/5 : LABEL version="1.0"

 ---> Using cache

Successfully built abcdef123456

Successfully tagged my-ml-model:1.0

This tags your local image with the full registry path so you can push it to your container registry for sharing or deployment.

Terminal

docker tag my-ml-model:1.0 myregistry.example.com/ml-project/my-ml-model:1.0

Expected Output

No output (command runs silently)

This uploads your tagged container image to the remote container registry so others or your deployment systems can access it.

Terminal

docker push myregistry.example.com/ml-project/my-ml-model:1.0

Expected Output

The push refers to repository [myregistry.example.com/ml-project/my-ml-model]

1.0: Pushed

This command downloads the container image from the registry to any machine where you want to run the ML model.

Terminal

docker pull myregistry.example.com/ml-project/my-ml-model:1.0

Expected Output

1.0: Pulling from ml-project/my-ml-model

Digest: sha256:abcdef1234567890

Status: Downloaded newer image for myregistry.example.com/ml-project/my-ml-model:1.0

Key Concept

If you remember nothing else from this pattern, remember: container registries let you store and share your ML model packages so they run the same everywhere.

Common Mistakes

Trying to push an image without tagging it with the registry path first.

Docker needs the full registry path to know where to upload the image.

Always tag your image with the registry URL before pushing.

Not logging into the container registry before pushing.

Registries require authentication to accept uploads.

Run 'docker login myregistry.example.com' and enter credentials before pushing.

Using the 'latest' tag for ML model images in production.

It can cause confusion about which version is deployed and make rollbacks hard.

Use explicit version tags like '1.0', '1.1' for clarity and control.

Summary

Build your ML model container image locally with 'docker build'.

Tag the image with your container registry path before pushing.

Push the image to the registry so it can be shared or deployed.

Pull the image from the registry on any machine to run the model.