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

Process Flow - Container registries for ML

Build ML Container Image

↓

Tag Image with Version

↓

Push Image to Registry

↓

Store Image in Registry

↓

Pull Image for Deployment or Testing

↓

Run Container from Image

↓

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.