Container registries for ML in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to tag a Docker image before pushing it to a container registry.

MLOps

docker tag my-ml-model:latest myregistry.azurecr.io/[1]

Drag options to blanks, or click blank then click option'

Amlmodel:v1

Blatest

Cmodel123

Dimage

Attempts:

3 left

💡 Hint

Common Mistakes

Using just 'latest' without the registry prefix

Using an invalid tag format

You need to tag the image with the registry name and a version tag like mlmodel:v1 before pushing.

2fill in blank

medium

Complete the command to push the tagged image to the container registry.

MLOps

docker push [1]

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Amyregistry/mlmodel

Bmy-ml-model:latest

Cmlmodel:v1

Dmyregistry.azurecr.io/mlmodel:v1

Attempts:

3 left

💡 Hint

Common Mistakes

Pushing without the registry URL

Using an incomplete image name

To push an image, you must specify the full registry path with the tag.

3fill in blank

hard

Fix the error in the command to login to an Azure container registry.

MLOps

az acr [1] --name myregistry

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Acreate

Bpush

Clogin

Dbuild

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'push' instead of 'login'

Using 'create' which is for creating registries

The correct command to login to an Azure Container Registry is az acr login.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters images with tag 'v1'.

MLOps

images = {name: tag for name, tag in image_list if tag [1] [2]

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A==

B'v1'

C'latest'

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' instead of '=='

Comparing to 'latest' instead of 'v1'

The comprehension filters images where the tag equals 'v1'.

5fill in blank

hard

Fill all three blanks to create a dictionary of image names in uppercase with their tags filtered by tag 'stable'.

MLOps

filtered_images = { [1]: [2] for name, [3] in images.items() if [2] == 'stable' }

Drag options to blanks, or click blank then click option'

Aname.upper()

Btag

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'name' instead of 'name.upper()' for keys

Mixing variable names in comprehension

The dictionary keys are the uppercase image names, values are tags, filtered by tag 'stable'.

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