Container registries for ML in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Purpose of Container Registries in ML Workflows

Why are container registries important in machine learning workflows?

A They store and manage container images that package ML models and dependencies for consistent deployment.

B They provide a platform to train ML models directly inside containers.

C They automatically generate ML model code from datasets.

D They replace the need for version control systems like Git in ML projects.

💡 Hint

Think about how ML models and their environments are shared and deployed.

Container registries store container images that include ML models and their dependencies. This ensures consistent environments across development, testing, and production.

💻 Command Output

intermediate

Docker Push Command Output for ML Model Image

What is the expected output when pushing an ML model container image named mlmodel:v1 to a Docker registry?

MLOps

docker push mlregistry.example.com/mlmodel:v1

A The command runs silently with no output.

B The image layers are uploaded with progress bars, ending with 'v1: digest: sha256:... size: ...' message.

C Syntax error: unexpected argument 'mlregistry.example.com/mlmodel:v1'.

D Error: image mlmodel:v1 not found locally.

💡 Hint

Consider what happens when Docker successfully uploads an image.

When pushing an image, Docker uploads each layer and shows progress bars. After completion, it shows a digest and size summary.

❓ Configuration

advanced

Configuring Access to a Private Container Registry for ML Deployment

Which configuration snippet correctly sets up Docker to authenticate to a private container registry mlregistry.example.com using a username and password?

A

{
  "auths": {
    "mlregistry.example.com": {
      "token": "dXNlcjpwYXNzd29yZA=="
    }
  }
}

B

{
  "auths": {
    "mlregistry.example.com": {
      "username": "user",
      "password": "password"
    }
  }
}

C

{
  "credentials": {
    "mlregistry.example.com": {
      "user": "user",
      "pass": "password"
    }
  }
}

D

{
  "auths": {
    "mlregistry.example.com": {
      "auth": "dXNlcjpwYXNzd29yZA=="
    }
  }
}

💡 Hint

Docker config uses base64 encoded auth strings under the auth key.

Docker config.json stores authentication as base64 encoded 'username:password' string under the 'auth' key inside 'auths'.

❓ Troubleshoot

advanced

Diagnosing Docker Pull Failure from ML Container Registry

You run docker pull mlregistry.example.com/mlmodel:v2 and get the error: unauthorized: authentication required. What is the most likely cause?

A You have not logged in to the private registry using <code>docker login</code>.

B The image tag <code>v2</code> does not exist in the registry.

C Your Docker daemon is not running.

D The Dockerfile used to build the image has syntax errors.

💡 Hint

Think about authentication steps before pulling from private registries.

The error indicates lack of authentication. You must log in to the private registry with docker login before pulling images.

🔀 Workflow

expert

Correct Sequence for ML Model Container Deployment Using Registry

Arrange the steps in the correct order to deploy an ML model container using a container registry.

A 2,1,3,4

B 1,3,2,4

C 1,2,3,4

D 3,2,1,4

💡 Hint

Think about the natural flow from building to running a container.

First build the image, then push it to the registry, pull it on the deployment server, and finally run it.