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.

Practice

1. What is the main purpose of a container registry in ML workflows?

easy

A. To train ML models faster using GPUs

B. To store and manage container images of ML models for easy sharing and deployment

C. To write code for ML models

D. To visualize ML model performance metrics

Solution

1. Step 1: Understand container registries

   Container registries are like libraries where container images are stored and managed.

2. Step 2: Connect to ML workflow

   In ML, container registries hold model containers so they can be shared and deployed easily.

3. Final Answer:

   To store and manage container images of ML models for easy sharing and deployment -> Option B

4. Quick Check:

   Container registry = store and share containers [OK]

Hint: Think of registries as storage for ML model containers [OK]

Common Mistakes:

• Confusing registries with training platforms
• Thinking registries run model code
• Mixing up registries with monitoring tools

2. Which of the following is the correct Docker command to push an ML model container tagged as v1.0 to a registry named mlregistry.example.com?

easy

A. docker push mlregistry.example.com/model:v1.0

B. docker pull mlregistry.example.com/model:v1.0

C. docker build mlregistry.example.com/model:v1.0

D. docker run mlregistry.example.com/model:v1.0

Solution

1. Step 1: Identify the push command

   The docker push command uploads a container image to a registry.

2. Step 2: Match the syntax

   The correct syntax is docker push [registry]/[image]:[tag], so docker push mlregistry.example.com/model:v1.0 is correct.

3. Final Answer:

   docker push mlregistry.example.com/model:v1.0 -> Option A

4. Quick Check:

   Push uploads image to registry [OK]

Hint: Push means upload; pull means download [OK]

Common Mistakes:

• Using pull instead of push to upload
• Confusing build with push
• Trying to run instead of push

3. Given the following commands, what will be the output of docker images after pushing the image?

docker build -t mlregistry.example.com/model:v1.0 .
docker push mlregistry.example.com/model:v1.0
docker images

medium

A. Shows the image mlregistry.example.com/model with tag v1.0 locally

B. Shows no images because push removes local images

C. Shows an error because push must come after images

D. Shows only images from Docker Hub

Solution

1. Step 1: Understand docker build and push

   docker build creates a local image tagged mlregistry.example.com/model:v1.0. docker push uploads it but does not delete local images.

2. Step 2: Check docker images output

   docker images lists local images, so it will show the built image with the tag v1.0.

3. Final Answer:

   Shows the image mlregistry.example.com/model with tag v1.0 locally -> Option A

4. Quick Check:

   Push uploads but keeps local image [OK]

Hint: Push uploads; local images stay until deleted [OK]

Common Mistakes:

• Assuming push deletes local images
• Thinking images command shows remote images
• Confusing command order effects

4. You tried to push your ML model container but got an error: denied: requested access to the resource is denied. What is the most likely cause?

medium

A. Your Dockerfile has syntax errors

B. You used the wrong tag format in docker build

C. You forgot to log in to the container registry before pushing

D. Your internet connection is too slow

Solution

1. Step 1: Understand the error meaning

   The error means you don't have permission to push to the registry, often due to missing login.

2. Step 2: Check common causes

   Not logging in with docker login is the most common cause of access denial.

3. Final Answer:

   You forgot to log in to the container registry before pushing -> Option C

4. Quick Check:

   Access denied usually means no login [OK]

Hint: Login first before pushing to registry [OK]

Common Mistakes:

• Blaming Dockerfile syntax for push errors
• Ignoring login step
• Assuming slow internet causes access denied

5. You want to maintain multiple versions of your ML model container in a registry. Which tagging strategy below is best practice?

hard

A. Push images without tags to save space

B. Use the same tag latest for all versions to simplify usage

C. Tag images with random numbers to avoid conflicts

D. Use semantic version tags like v1.0, v1.1, and v2.0 for each container image

Solution

1. Step 1: Understand tagging purpose

   Tags help identify versions clearly. Semantic versioning is a clear, organized method.

2. Step 2: Evaluate options

   Using latest only hides older versions. Random tags cause confusion. No tags default to latest, losing version control.

3. Final Answer:

   Use semantic version tags like v1.0, v1.1, and v2.0 for each container image -> Option D

4. Quick Check:

   Semantic version tags = best version control [OK]

Hint: Use clear version tags, not just 'latest' [OK]

Common Mistakes:

• Using only 'latest' tag losing version history
• Random tags causing confusion
• Pushing untagged images