Skip to content

Chapter 3.4 - Build and publish the model with BentoML and Docker in the CI/CD pipeline

Introduction

In this chapter, you will containerize and push the model to the container registry with the help of the CI/CD pipeline. You will use BentoML and Docker to containerize and publish the model and the pipeline to trigger the publishing.

The steps will be similar to the last chapter, but we will use the pipeline to automate the process.

In this chapter, you will learn how to:

  1. Grant access to the container registry on the cloud provider for the CI/CD pipeline
  2. Store the container registry credentials in the CI/CD configuration
  3. Create the CI/CD pipeline for publishing the model to the container registry

The following diagram illustrates the control flow of the experiment at the end of this chapter:

flowchart TB
    dot_dvc[(.dvc)] <-->|dvc pull
                         dvc push| s3_storage[(S3 Storage)]
    dot_git[(.git)] <-->|git pull
                         git push| repository[(Repository)]
    workspaceGraph <-....-> dot_git
    data[data/raw]

    subgraph cacheGraph[CACHE]
        dot_dvc
        dot_git
    end

    subgraph workspaceGraph[WORKSPACE]
        data --> code[*.py]
        subgraph dvcGraph["dvc.yaml"]
            code
        end
        params[params.yaml] -.- code
        code <--> bento_model[classifier.bentomodel]
        subgraph bentoGraph[bentofile.yaml]
            bento_model
            serve[serve.py] <--> bento_model
        end
        bento_model <-.-> dot_dvc
    end

    subgraph remoteGraph[REMOTE]
        s3_storage
        subgraph gitGraph[Git Remote]
            repository <--> |...|action[Action]
        end
        registry[(Container
                  registry)]
        action --> |bentoml build
                    bentoml containerize
                    docker push|registry
    end

    style workspaceGraph opacity:0.4,color:#7f7f7f80
    style dvcGraph opacity:0.4,color:#7f7f7f80
    style cacheGraph opacity:0.4,color:#7f7f7f80
    style data opacity:0.4,color:#7f7f7f80
    style dot_git opacity:0.4,color:#7f7f7f80
    style dot_dvc opacity:0.4,color:#7f7f7f80
    style code opacity:0.4,color:#7f7f7f80
    style bentoGraph opacity:0.4,color:#7f7f7f80
    style serve opacity:0.4,color:#7f7f7f80
    style bento_model opacity:0.4,color:#7f7f7f80
    style params opacity:0.4,color:#7f7f7f80
    style s3_storage opacity:0.4,color:#7f7f7f80
    style remoteGraph opacity:0.4,color:#7f7f7f80
    style gitGraph opacity:0.4,color:#7f7f7f80
    style repository opacity:0.4,color:#7f7f7f80
    linkStyle 0 opacity:0.4,color:#7f7f7f80
    linkStyle 1 opacity:0.4,color:#7f7f7f80
    linkStyle 2 opacity:0.4,color:#7f7f7f80
    linkStyle 3 opacity:0.4,color:#7f7f7f80
    linkStyle 4 opacity:0.4,color:#7f7f7f80
    linkStyle 5 opacity:0.4,color:#7f7f7f80
    linkStyle 6 opacity:0.4,color:#7f7f7f80
    linkStyle 7 opacity:0.4,color:#7f7f7f80
    linkStyle 8 opacity:0.4,color:#7f7f7f80

Steps

Set up access to the container registry of the cloud provider

The container registry will need to be accessed inside the CI/CD pipeline to push the Docker image.

This is the same process you did for DVC as described in Chapter 8 - Reproduce the ML experiment in a CI/CD pipeline but this time for the container registry.

Update the Google Service Account and its associated Google Service Account Key to access Google Cloud from the CI/CD pipeline without your own credentials.

Execute the following command(s) in a terminal
1
2
3
4
5
6
7
8
9
# Set the Cloud Storage permissions for the Google Service Account
gcloud projects add-iam-policy-binding $GCP_PROJECT_ID \
    --member="serviceAccount:google-service-account@${GCP_PROJECT_ID}.iam.gserviceaccount.com" \
    --role="roles/storage.objectAdmin"

# Set the Artifact Registry permissions for the Google Service Account
gcloud projects add-iam-policy-binding $GCP_PROJECT_ID \
    --member="serviceAccount:google-service-account@${GCP_PROJECT_ID}.iam.gserviceaccount.com" \
    --role="roles/artifactregistry.createOnPushWriter"

Tip

There is no need to update the value in the CI/CD pipeline configuration.

All changes are made at the Google Cloud level and the key file is not changed.

This guide has been written with Google Cloud in mind. We are open to contributions to add support for other cloud providers such as Amazon Web Services, Exoscale, Microsoft Azure or Self-hosted Kubernetes but we might not officially support them.

If you want to contribute, please open an issue or a pull request on the GitHub repository. Your help is greatly appreciated!

Add container registry CI/CD secrets

Add the container registry secret to access the container registry from the CI/CD pipeline. Depending on the CI/CD platform you are using, the process will be different:

Create the following new variables by going to the Settings section from the top header of your GitHub repository. Select Secrets and variables > Actions and select New repository secret:

  • GCP_CONTAINER_REGISTRY_HOST: The host of the container registry (ex: europe-west6-docker.pkg.dev/mlops-surname-project/mlops-surname-registry, from the variable GCP_CONTAINER_REGISTRY_HOST in the previous chapter)

Save the variables by selecting Add secret.

Create the following new variables by going to Settings > CI/CD from the left sidebar of your GitLab project. Select Variables and select Add variable:

  • GCP_CONTAINER_REGISTRY_HOST: The host of the container registry (ex: europe-west6-docker.pkg.dev/mlops-surname-project/mlops-surname-registry, from the variable GCP_CONTAINER_REGISTRY_HOST in the previous chapter)
    • Protect variable: Unchecked
    • Mask variable: Checked
    • Expand variable reference: Unchecked

Save the variables by selecting Add secret.

This guide has been written with Google Cloud in mind. We are open to contributions to add support for other cloud providers such as Amazon Web Services, Exoscale, Microsoft Azure or Self-hosted Kubernetes but we might not officially support them.

If you want to contribute, please open an issue or a pull request on the GitHub repository. Your help is greatly appreciated!

Update the CI/CD pipeline configuration file

You will adjust the pipeline to build and push the the docker image to the container registry. The following steps will be performed:

  1. Detect a new commit on the main branch
  2. Authenticate to the cloud provider
  3. Build the Docker image
  4. Push the Docker image to the container registry

Update the .github/workflows/mlops.yaml file with the following content.

Take some time to understand the deploy job and its steps:

.github/workflows/mlops.yaml
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
name: MLOps

on:
  # Runs on pushes targeting main branch
  push:
    branches:
      - main

  # Runs on pull requests
  pull_request:

  # Allows you to run this workflow manually from the Actions tab
  workflow_dispatch:

jobs:
  train-report-and-publish:
    permissions: write-all
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
      - name: Setup Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.12'
          cache: pip
      - name: Install dependencies
        run: pip install --requirement requirements-freeze.txt
      - name: Login to Google Cloud
        uses: google-github-actions/auth@v2
        with:
          credentials_json: '${{ secrets.GOOGLE_SERVICE_ACCOUNT_KEY }}'
      - name: Train model
        run: dvc repro --pull
      - name: Setup CML
        if: github.event_name == 'pull_request'
        uses: iterative/setup-cml@v2
        with:
          version: '0.20.6'
      - name: Create CML report
        if: github.event_name == 'pull_request'
        env:
          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          # Fetch all other Git branches
          git fetch --depth=1 origin main:main

          # Add title to the report
          echo "# Experiment Report (${{ github.sha }})" >> report.md

          # Compare parameters to main branch
          echo "## Params workflow vs. main" >> report.md
          dvc params diff main --md >> report.md

          # Compare metrics to main branch
          echo "## Metrics workflow vs. main" >> report.md
          dvc metrics diff main --md >> report.md

          # Compare plots (images) to main branch
          dvc plots diff main

          # Create plots
          echo "## Plots" >> report.md

          # Create training history plot
          echo "### Training History" >> report.md
          echo "#### main" >> report.md
          echo '![](./dvc_plots/static/main_evaluation_plots_training_history.png "Training History")' >> report.md
          echo "#### workspace" >> report.md
          echo '![](./dvc_plots/static/workspace_evaluation_plots_training_history.png "Training History")' >> report.md

          # Create predictions preview
          echo "### Predictions Preview" >> report.md
          echo "#### main" >> report.md
          echo '![](./dvc_plots/static/main_evaluation_plots_pred_preview.png "Predictions Preview")' >> report.md
          echo "#### workspace" >> report.md
          echo '![](./dvc_plots/static/workspace_evaluation_plots_pred_preview.png "Predictions Preview")' >> report.md

          # Create confusion matrix
          echo "### Confusion Matrix" >> report.md
          echo "#### main" >> report.md
          echo '![](./dvc_plots/static/main_evaluation_plots_confusion_matrix.png "Confusion Matrix")' >> report.md
          echo "#### workspace" >> report.md
          echo '![](./dvc_plots/static/workspace_evaluation_plots_confusion_matrix.png "Confusion Matrix")' >> report.md

          # Publish the CML report
          cml comment update --target=pr --publish report.md
      - name: Log in to the Container registry
        uses: docker/login-action@v3
        with:
          registry: ${{ secrets.GCP_CONTAINER_REGISTRY_HOST }}
          username: _json_key
          password: ${{ secrets.GOOGLE_SERVICE_ACCOUNT_KEY }}
      - name: Import the BentoML model
        if: github.ref == 'refs/heads/main'
        run: bentoml models import model/celestial_bodies_classifier_model.bentomodel
      - name: Build the BentoML model artifact
        if: github.ref == 'refs/heads/main'
        run: bentoml build src
      - name: Containerize and publish the BentoML model artifact Docker image
        if: github.ref == 'refs/heads/main'
        run: |
          # Containerize the Bento
          bentoml containerize celestial_bodies_classifier:latest \
            --image-tag ${{ secrets.GCP_CONTAINER_REGISTRY_HOST }}/celestial-bodies-classifier:latest \
            --image-tag ${{ secrets.GCP_CONTAINER_REGISTRY_HOST }}/celestial-bodies-classifier:${{ github.sha }}
          # Push the container to the Container Registry
          docker push --all-tags ${{ secrets.GCP_CONTAINER_REGISTRY_HOST }}/celestial-bodies-classifier

Check the differences with Git to validate the changes.

Execute the following command(s) in a terminal
1
2
# Show the differences with Git
git diff .github/workflows/mlops.yaml

The output should be similar to this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
diff --git a/.github/workflows/mlops.yaml b/.github/workflows/mlops.yaml
index 1fa989b..6d479ef 100644
--- a/.github/workflows/mlops.yaml
+++ b/.github/workflows/mlops.yaml
@@ -13,7 +13,7 @@ on:
   workflow_dispatch:

 jobs:
-  train-and-report:
+  train-report-and-publish:
     permissions: write-all
     runs-on: ubuntu-latest
     steps:
@@ -85,3 +85,43 @@ jobs:

           # Publish the CML report
           cml comment update --target=pr --publish report.md
+      - name: Log in to the Container registry
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ secrets.GCP_CONTAINER_REGISTRY_HOST }}
+          username: _json_key
+          password: ${{ secrets.GOOGLE_SERVICE_ACCOUNT_KEY }}
+      - name: Import the BentoML model
+        if: github.ref == 'refs/heads/main'
+        run: bentoml models import model/celestial_bodies_classifier_model.bentomodel
+      - name: Build the BentoML model artifact
+        if: github.ref == 'refs/heads/main'
+        run: bentoml build src
+      - name: Containerize and publish the BentoML model artifact Docker image
+        if: github.ref == 'refs/heads/main'
+        run: |
+          # Containerize the Bento
+          bentoml containerize celestial_bodies_classifier:latest \
+            --image-tag ${{ secrets.GCP_CONTAINER_REGISTRY_HOST }}/celestial-bodies-classifier:latest \
+            --image-tag ${{ secrets.GCP_CONTAINER_REGISTRY_HOST }}/celestial-bodies-classifier:${{ github.sha }}
+          # Push the container to the Container Registry
+          docker push --all-tags ${{ secrets.GCP_CONTAINER_REGISTRY_HOST }}/celestial-bodies-classifier

Update the CI/CD pipeline configuration file

Update the .gitlab-ci.yml file to add a new stage to deploy the model on the Kubernetes cluster.

Take some time to understand the publish and deploy job and its steps.

.gitlab-ci.yml
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
stages:
  - train
  - report
  - publish
  - deploy

variables:
  # Change pip's cache directory to be inside the project directory since we can
  # only cache local items.
  PIP_CACHE_DIR: "$CI_PROJECT_DIR/.cache/pip"
  # https://dvc.org/doc/user-guide/troubleshooting?tab=GitLab-CI-CD#git-shallow
  GIT_DEPTH: "0"
  # Set the path to Google Service Account key for DVC - https://dvc.org/doc/command-reference/remote/add#google-cloud-storage
  GOOGLE_APPLICATION_CREDENTIALS: "${CI_PROJECT_DIR}/google-service-account-key.json"
  # Environment variable for CML
  REPO_TOKEN: $GITLAB_PAT

train:
  stage: train
  image: python:3.12
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"
  before_script:
    # Set the Google Service Account key
    - echo "${GOOGLE_SERVICE_ACCOUNT_KEY}" | base64 -d > $GOOGLE_APPLICATION_CREDENTIALS
    # Create the virtual environment for caching
    - python3.12 -m venv .venv
    - source .venv/bin/activate
  script:
    # Install dependencies
    - pip install --requirement requirements-freeze.txt
    # Run the experiment
    - dvc repro --pull
  cache:
    paths:
      # Pip's cache doesn't store the Python packages
      # https://pip.pypa.io/en/stable/reference/pip_install/#caching
      - .cache/pip
      - .venv/

report:
  stage: report
  image: iterativeai/cml:0-dvc3-base1
  needs:
    - train
  rules:
    - if: $CI_PIPELINE_SOURCE == "merge_request_event"
  before_script:
    # Set the Google Service Account key
    - echo "${GOOGLE_SERVICE_ACCOUNT_KEY}" | base64 -d > $GOOGLE_APPLICATION_CREDENTIALS
  script:
    - |
      # Fetch the experiment changes
      dvc pull

      # Fetch all other Git branches
      git fetch --depth=1 origin main:main

      # Add title to the report
      echo "# Experiment Report (${CI_COMMIT_SHA})" >> report.md

      # Compare parameters to main branch
      echo "## Params workflow vs. main" >> report.md
      dvc params diff main --md >> report.md

      # Compare metrics to main branch
      echo "## Metrics workflow vs. main" >> report.md
      dvc metrics diff main --md >> report.md

      # Compare plots (images) to main branch
      dvc plots diff main

      # Create plots
      echo "## Plots" >> report.md

      # Create training history plot
      echo "### Training History" >> report.md
      echo "#### main" >> report.md
      echo '![](./dvc_plots/static/main_evaluation_plots_training_history.png "Training History")' >> report.md
      echo "#### workspace" >> report.md
      echo '![](./dvc_plots/static/workspace_evaluation_plots_training_history.png "Training History")' >> report.md

      # Create predictions preview
      echo "### Predictions Preview" >> report.md
      echo "#### main" >> report.md
      echo '![](./dvc_plots/static/main_evaluation_plots_pred_preview.png "Predictions Preview")' >> report.md
      echo "#### workspace" >> report.md
      echo '![](./dvc_plots/static/workspace_evaluation_plots_pred_preview.png "Predictions Preview")' >> report.md

      # Create confusion matrix
      echo "### Confusion Matrix" >> report.md
      echo "#### main" >> report.md
      echo '![](./dvc_plots/static/main_evaluation_plots_confusion_matrix.png "Confusion Matrix")' >> report.md
      echo "#### workspace" >> report.md
      echo '![](./dvc_plots/static/workspace_evaluation_plots_confusion_matrix.png "Confusion Matrix")' >> report.md

      # Publish the CML report
      cml comment update --target=pr --publish report.md

publish:
  stage: publish
  image: python:3.12
  needs:
    - train
  rules:
    - if: $CI_COMMIT_BRANCH == "main"
  services:
    - docker:25.0.3-dind
  variables:
    DOCKER_HOST: "tcp://docker:2375"
  before_script:
    # Install Docker
    - apt-get update
    - apt-get install --yes ca-certificates curl
    - install -m 0755 -d /etc/apt/keyrings
    - curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc
    - chmod a+r /etc/apt/keyrings/docker.asc
    - echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null
    - apt-get update
    - apt-get install --yes docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
    # Set the Google Service Account key
    - echo "${GOOGLE_SERVICE_ACCOUNT_KEY}" | base64 -d > $GOOGLE_APPLICATION_CREDENTIALS
    # Login to the Google Container Registry
    - cat "${GOOGLE_APPLICATION_CREDENTIALS}" | docker login -u _json_key --password-stdin "${GCP_CONTAINER_REGISTRY_HOST}"
    # Create the virtual environment for caching
    - python3.12 -m venv .venv
    - source .venv/bin/activate
  script:
    # Install dependencies
    - pip install --requirement requirements-freeze.txt
    # Pull the BentoML model artifact
    - dvc pull model/celestial_bodies_classifier_model.bentomodel
    # Import the BentoML model
    - bentoml models import model/celestial_bodies_classifier_model.bentomodel
    # Build the BentoML model artifact
    - bentoml build src
    # Containerize and publish the BentoML model artifact Docker image
    - |
      bentoml containerize celestial_bodies_classifier:latest \
        --image-tag $GCP_CONTAINER_REGISTRY_HOST/celestial-bodies-classifier:latest \
        --image-tag $GCP_CONTAINER_REGISTRY_HOST/celestial-bodies-classifier:${CI_COMMIT_SHA}
    # Push the container to the Container Registry
    - docker push --all-tags $GCP_CONTAINER_REGISTRY_HOST/celestial-bodies-classifier
  cache:
    paths:
      # Pip's cache doesn't store the Python packages
      # https://pip.pypa.io/en/stable/reference/pip_install/#caching
      - .cache/pip
      - .venv/

Check the differences with Git to validate the changes.

Execute the following command(s) in a terminal
1
2
# Show the differences with Git
git diff .gitlab-ci.yml

The output should be similar to this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index dbf3b25..7dcdfe7 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,6 +1,8 @@
 stages:
   - train
   - report
+  - publish

 variables:
   # Change pip's cache directory to be inside the project directory since we can
@@ -95,3 +97,73 @@ report:

       # Publish the CML report
       cml comment update --target=pr --publish report.md
+
+publish:
+  stage: publish
+  image: python:3.12
+  needs:
+    - train
+  rules:
+    - if: $CI_COMMIT_BRANCH == "main"
+  services:
+    - docker:25.0.3-dind
+  variables:
+    DOCKER_HOST: "tcp://docker:2375"
+  before_script:
+    # Install Docker
+    - apt-get update
+    - apt-get install --yes ca-certificates curl
+    - install -m 0755 -d /etc/apt/keyrings
+    - curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc
+    - chmod a+r /etc/apt/keyrings/docker.asc
+    - echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | tee /etc/apt/sources.list.d/docker.list > /dev/null
+    - apt-get update
+    - apt-get install --yes docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
+    # Set the Google Service Account key
+    - echo "${GOOGLE_SERVICE_ACCOUNT_KEY}" | base64 -d > $GOOGLE_APPLICATION_CREDENTIALS
+    # Login to the Google Container Registry
+    - cat "${GOOGLE_APPLICATION_CREDENTIALS}" | docker login -u _json_key --password-stdin "${GCP_CONTAINER_REGISTRY_HOST}"
+    # Create the virtual environment for caching
+    - python3.12 -m venv .venv
+    - source .venv/bin/activate
+  script:
+    # Install dependencies
+    - pip install --requirement requirements-freeze.txt
+    # Pull the BentoML model artifact
+    - dvc pull model/celestial_bodies_classifier_model.bentomodel
+    # Import the BentoML model
+    - bentoml models import model/celestial_bodies_classifier_model.bentomodel
+    # Build the BentoML model artifact
+    - bentoml build src
+    # Containerize and publish the BentoML model artifact Docker image
+    - |
+      bentoml containerize celestial_bodies_classifier:latest \
+        --image-tag $GCP_CONTAINER_REGISTRY_HOST/celestial-bodies-classifier:latest \
+        --image-tag $GCP_CONTAINER_REGISTRY_HOST/celestial-bodies-classifier:${CI_COMMIT_SHA}
+    # Push the container to the Container Registry
+    - docker push --all-tags $GCP_CONTAINER_REGISTRY_HOST/celestial-bodies-classifier
+  cache:
+    paths:
+      # Pip's cache doesn't store the Python packages
+      # https://pip.pypa.io/en/stable/reference/pip_install/#caching
+      - .cache/pip
+      - .venv/

Check the changes

Check the changes with Git to ensure that all the necessary files are tracked:

Execute the following command(s) in a terminal
1
2
3
4
5
# Add all the files
git add .

# Check the changes
git status

The output should look similar to this:

1
2
3
4
5
6
On branch main
Your branch is up to date with 'origin/main'.

Changes to be committed:
(use "git restore --staged <file>..." to unstage)
    modified:   .github/workflows/mlops.yaml

Commit the changes to Git

Commit the changes to Git.

Execute the following command(s) in a terminal
1
2
3
4
5
# Commit the changes
git commit -m "Use the pipeline to containerize and publish the model artifact to the model registry"

# Push the changes
git push

Summary

Congratulations! You have successfully prepared the model for automated deployment in a production environment with the CI/CD pipeline!

New versions of the model will be published to the artifact registry automatically as soon as they are pushed to the main branch.

In this chapter, you have successfully:

  1. Automated the containerization and publication of the BentoML model artifact to the container registry

State of the MLOps process

  • Notebook has been transformed into scripts for production
  • Codebase and dataset are versioned
  • Steps used to create the model are documented and can be re-executed
  • Changes done to a model can be visualized with parameters, metrics and plots to identify differences between iterations
  • Codebase can be shared and improved by multiple developers
  • Dataset can be shared among the developers and is placed in the right directory in order to run the experiment
  • Experiment can be executed on a clean machine with the help of a CI/CD pipeline
  • CI/CD pipeline is triggered on pull requests and reports the results of the experiment
  • Changes to model can be thoroughly reviewed and discussed before integrating them into the codebase
  • Model can be saved and loaded with all required artifacts for future usage
  • Model can be easily used outside of the experiment context
  • Model publication to the artifact registry is automated
  • Model is accessible from the Internet and can be used anywhere
  • Model requires manual deployment on the cluster
  • Model cannot be trained on hardware other than the local machine
  • Model cannot be trained on custom hardware for specific use-cases

You will address these issues in the next chapters for improved efficiency and collaboration. Continue the guide to learn how.

Sources

Highly inspired by: