Container registries for ML in MLOps - Step-by-Step Execution

Process Flow - Container registries for ML

Build ML Container Image

↓

Tag Image with Version

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Push Image to Registry

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Store Image in Registry

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Pull Image for Deployment or Testing

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Run Container from Image

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Done

This flow shows how an ML container image is built, tagged, pushed to a registry, stored, pulled later, and run for deployment or testing.

Execution Sample

MLOps

docker build -t mlmodel:1.0 .
docker tag mlmodel:1.0 registry.example.com/mlproject/mlmodel:1.0
docker push registry.example.com/mlproject/mlmodel:1.0
docker pull registry.example.com/mlproject/mlmodel:1.0
docker run registry.example.com/mlproject/mlmodel:1.0

This sequence builds an ML model container image, tags it with a registry path, pushes it to the registry, pulls it back, and runs it.

Process Table

Step	Action	Image Name/Tag	Registry Interaction	Result
1	Build image	mlmodel:1.0	None	Local image mlmodel:1.0 created
2	Tag image	registry.example.com/mlproject/mlmodel:1.0	None	Image tagged for registry
3	Push image	registry.example.com/mlproject/mlmodel:1.0	Push to registry	Image uploaded to registry
4	Pull image	registry.example.com/mlproject/mlmodel:1.0	Pull from registry	Image downloaded locally
5	Run container	registry.example.com/mlproject/mlmodel:1.0	None	Container started from image
6	Exit	N/A	N/A	Process complete

💡 All steps completed successfully; container running from registry image

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4	After Step 5	Final
Local Image Store	empty	mlmodel:1.0	mlmodel:1.0 + registry.example.com/mlproject/mlmodel:1.0 tag	mlmodel:1.0 + tagged image pushed	mlmodel:1.0 + tagged image pulled	container running from pulled image	container running
Registry Storage	empty	empty	image registry.example.com/mlproject/mlmodel:1.0 stored	image stored	image stored	image stored	image stored

Key Moments - 3 Insights

Why do we tag the image with the registry path before pushing?

Tagging the image with the registry path (step 2) tells Docker where to push the image. Without this, Docker won't know which registry to upload to, as shown in execution_table row 2.

What happens if we try to run the container without pulling the image first?

If the image is not present locally, Docker will automatically pull it from the registry before running. Step 4 and 5 show pulling then running, but Docker can combine these steps if needed.

Why do we push the image to a registry in ML workflows?

Pushing to a registry stores the image centrally so teams can share, deploy, and reproduce ML environments easily, as seen in execution_table step 3 and variable_tracker Registry Storage.

Visual Quiz - 3 Questions

Look at the execution table, what is the image tag used when pushing to the registry?

A registry.example.com/mlproject/mlmodel:1.0

B mlmodel:1.0

C mlproject/mlmodel:latest

D registry.example.com/mlmodel:latest

💡 Hint

Check the 'Image Name/Tag' column at step 3 in the execution_table.

At which step does the image get stored in the registry?

A Step 1

B Step 2

C Step 3

D Step 4

💡 Hint

Look at the 'Registry Interaction' column to find when the push happens.

If the image was not tagged before pushing, what would happen?

A Docker would push to the default registry automatically

B Push would fail because no registry path is specified

C Image would be pushed with the local tag

D Image would be deleted

💡 Hint

Refer to key_moments about tagging before pushing and execution_table step 2.

Concept Snapshot

Container registries store ML container images centrally.
Build your ML image locally with docker build.
Tag the image with the registry path before pushing.
Push uploads the image to the registry.
Pull downloads the image for deployment or testing.
Run starts a container from the pulled image.

Full Transcript

This visual execution shows how ML container images move from local build to registry storage and back for deployment. First, you build the image locally with a tag. Then you tag it with the registry path so Docker knows where to push it. Next, you push the image to the registry, storing it centrally. Later, you pull the image back from the registry to your local machine. Finally, you run a container from the pulled image. This process helps teams share and deploy ML environments easily and reproducibly.

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