Container registries for ML in MLOps - Mini Project: Build & Apply

Container Registries for ML

📖 Scenario: You are working on a machine learning project where you need to package your ML model into a container. To share and deploy this container easily, you will use a container registry. This project will guide you through creating a simple container image tag list, setting up a registry URL, filtering images by a tag prefix, and finally displaying the filtered images ready for deployment.

🎯 Goal: Build a small program that manages ML container image tags, filters them by a prefix, and prints the filtered list. This simulates preparing ML containers for deployment using a container registry.

📋 What You'll Learn

Create a dictionary of ML container images with exact tags and versions

Add a variable for the container registry URL

Filter the images to only those starting with a specific prefix

Print the filtered list of image tags

💡 Why This Matters

🌍 Real World

Container registries store and manage container images for ML models, making it easy to share and deploy models consistently.

💼 Career

Understanding container registries and filtering images is essential for ML engineers and DevOps professionals working on model deployment pipelines.

1

Create ML container images dictionary

Create a dictionary called ml_images with these exact entries: 'model_v1': 'ml-model:1.0', 'model_v2': 'ml-model:2.0', 'data_prep': 'data-prep:latest', 'model_experiment': 'ml-model-experiment:0.1'

MLOps

# Create the ml_images dictionary with exact keys and values
# Your code here

Hint

Use curly braces {} to create a dictionary with the exact keys and values given.

2

Add container registry URL

Add a variable called registry_url and set it to the string 'registry.example.com/ml'

MLOps

ml_images = {
    'model_v1': 'ml-model:1.0',
    'model_v2': 'ml-model:2.0',
    'data_prep': 'data-prep:latest',
    'model_experiment': 'ml-model-experiment:0.1'
}
# Add the registry_url variable below
# Your code here

Hint

Assign the exact string to the variable registry_url.

3

Filter images by prefix

Create a list called filtered_images that contains only the image tags from ml_images values starting with the prefix 'ml-model:'. Use a list comprehension.

MLOps

ml_images = {
    'model_v1': 'ml-model:1.0',
    'model_v2': 'ml-model:2.0',
    'data_prep': 'data-prep:latest',
    'model_experiment': 'ml-model-experiment:0.1'
}
registry_url = 'registry.example.com/ml'
# Create filtered_images list with tags starting with 'ml-model:'
# Your code here

Hint

Use a list comprehension to check each tag in ml_images.values() and keep only those starting with 'ml-model:'.

4

Print filtered image tags

Print the filtered_images list to display the filtered ML container image tags.

MLOps

ml_images = {
    'model_v1': 'ml-model:1.0',
    'model_v2': 'ml-model:2.0',
    'data_prep': 'data-prep:latest',
    'model_experiment': 'ml-model-experiment:0.1'
}
registry_url = 'registry.example.com/ml'
filtered_images = [tag for tag in ml_images.values() if tag.startswith('ml-model:')]
# Print the filtered_images list below
# Your code here

Hint

Use print(filtered_images) to show the filtered list.

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