Merge pull request #54514 from JeffKoMS/video-acr

Added videos to the content

Author: v-dirichards

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/1-introduction.yml

@@ -8,6 +8,7 @@ metadata:
   author: jeffkoms
   ms.author: jeffko
   ms.topic: unit
+  zone_pivot_groups: video-or-text
 durationInMinutes: 3
 content: |
   [!include[](includes/1-introduction.md)]

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/2-image-storage.yml

@@ -8,6 +8,7 @@ metadata:
   author: jeffkoms
   ms.author: jeffko
   ms.topic: unit
+  zone_pivot_groups: video-or-text
 durationInMinutes: 8
 content: |
   [!include[](includes/2-image-storage.md)]

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/3-build-run-acr-tasks.yml

@@ -8,6 +8,7 @@ metadata:
   author: jeffkoms
   ms.author: jeffko
   ms.topic: unit
+  zone_pivot_groups: video-or-text
 durationInMinutes: 10
 content: |
   [!include[](includes/3-build-run-acr-tasks.md)]

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/4-tag-version-images.yml

@@ -8,6 +8,7 @@ metadata:
   author: jeffkoms
   ms.author: jeffko
   ms.topic: unit
+  zone_pivot_groups: video-or-text
 durationInMinutes: 8
 content: |
   [!include[](includes/4-tag-version-images.md)]

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/7-summary.yml

@@ -8,6 +8,7 @@ metadata:
   author: jeffkoms
   ms.author: jeffko
   ms.topic: unit
+  zone_pivot_groups: video-or-text
 durationInMinutes: 2
 content: |
   [!include[](includes/7-summary.md)]

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/includes/1-introduction.md

@@ -1,3 +1,11 @@
+::: zone pivot="video"
+
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=d9fd1af7-7d8b-4e29-924b-16830aef386b]
+
+::: zone-end
+
+::: zone pivot="text"
+
 AI applications require a secure, scalable location to store container images that serve inference APIs, data pipelines, and backend services. This module guides you through using Azure Container Registry (ACR) to store, build, and manage container images for AI solutions on Azure.
 
 Imagine you're a developer building an AI inference service that processes customer requests in real time. Your team maintains several container images: a model serving API, a preprocessing service, and a monitoring sidecar. Each image requires updates as models improve and dependencies change. Currently, your team builds images locally and pushes them from developer workstations, leading to inconsistent builds and unclear versioning. Your client expects reliable deployments with traceable image versions and the ability to roll back to previous releases. You need a centralized registry that supports cloud-based builds, enforces consistent tagging, and integrates with your deployment pipelines.
@@ -10,3 +18,8 @@ After completing this module, you'll be able to:
 - Build and manage container images in the cloud using ACR Tasks
 - Implement tagging and versioning strategies for reliable container deployments
 - Use the Azure CLI to manage container images and run ACR quick tasks
+
+::: zone-end
+
+> [!NOTE]
+> We recognize that different people like to learn in different ways. You can choose to complete this module in video-based format or you can read the content as text and images. The text contains greater detail than the videos, so in some cases you might want to refer to it as supplemental material to the video presentation.

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/includes/2-image-storage.md

@@ -1,3 +1,14 @@
+::: zone pivot="video"
+
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=3808428a-8e36-4ec9-9195-ea1e9db356cc]
+
+> [!TIP]
+> Select the **Text** tab for more details!
+
+::: zone-end
+
+::: zone pivot="text"
+
 Understanding how Azure Container Registry (ACR) organizes container images helps you design an effective storage strategy for AI applications. This unit explains the registry hierarchy and shows how manifests, layers, and digests enable efficient storage and retrieval of container artifacts.
 
 ## Understand Azure Container Registry
@@ -101,6 +112,8 @@ Following these practices helps you maintain an organized and efficient registry
 - **Enable geo-replication:** For global AI deployments, replicate images to regions where your services run. This reduces pull latency and improves deployment reliability.
 - **Monitor storage:** Track storage consumption and implement retention policies to remove untagged manifests. Container images accumulate over time, and cleanup policies prevent unnecessary storage costs.
 
+::: zone-end
+
 ## Additional resources
 
 - [About registries, repositories, and artifacts](/azure/container-registry/container-registry-concepts)

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/includes/3-build-run-acr-tasks.md

@@ -1,3 +1,14 @@
+::: zone pivot="video"
+
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=7e2b484f-dbc3-43dd-b55c-38b002e11267]
+
+> [!TIP]
+> Select the **Text** tab for more details!
+
+::: zone-end
+
+::: zone pivot="text"
+
 ACR Tasks provide cloud-based container image building without requiring a local Docker installation. This capability addresses common challenges for AI development teams, including inconsistent builds across developer machines and the need for automated continuous integration. This unit covers quick tasks for on-demand builds, automatic triggers for continuous integration, and multi-step workflows for complex build scenarios.
 
 ## Understand ACR Tasks
@@ -164,6 +175,8 @@ Follow these practices to get the most from ACR Tasks:
 - **Optimize build context:** Use `.dockerignore` to exclude unnecessary files from the context upload. Large contexts slow down builds and consume bandwidth.
 - **Layer caching:** ACR Tasks cache layers between builds. Order Dockerfile instructions to maximize cache hits by placing frequently changing instructions late in the file.
 
+::: zone-end
+
 ## Additional resources
 
 - [ACR Tasks overview](/azure/container-registry/container-registry-tasks-overview)

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/includes/4-tag-version-images.md

@@ -1,3 +1,14 @@
+::: zone pivot="video"
+
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=af2835eb-5226-4c86-b3dc-26faf5c5e96c]
+
+> [!TIP]
+> Select the **Text** tab for more details!
+
+::: zone-end
+
+::: zone pivot="text"
+
 The tagging strategy you choose affects deployment reliability, rollback capabilities, and image maintenance. This unit covers tagging approaches, versioning schemes, and lifecycle management practices that support production AI deployments.
 
 ## Understand tagging strategies
@@ -196,6 +207,8 @@ Follow these practices to maintain a reliable container image strategy:
 - **Include build metadata:** Add traceability information to tags for debugging and auditing. Link images to builds and source commits.
 - **Document your tagging scheme:** Ensure your team follows consistent conventions. Document which tags are stable versus unique and when to use each.
 
+::: zone-end
+
 ## Additional resources
 
 - [Recommendations for tagging and versioning container images](/azure/container-registry/container-registry-image-tag-version)

learn-pr/wwl-data-ai/store-manage-containers-azure-container-registry/includes/7-summary.md

@@ -1,5 +1,15 @@
+::: zone pivot="video"
+
+>[!VIDEO https://learn-video.azurefd.net/vod/player?id=05ae3e04-d6e8-4dc9-9e43-d299fbd24d70]
+
+::: zone-end
+
+::: zone pivot="text"
+
 In this module, you learned how Azure Container Registry (ACR) organizes container images using a hierarchy of registries, repositories, and artifacts. You explored how manifests, layers, and digests enable efficient storage and precise image identification. Tags provide human-readable references, while digests offer immutable identifiers for guaranteed reproducibility. You also learned to use ACR Tasks to build container images in the cloud without requiring a local Docker installation. Quick tasks enable on-demand builds from local files or Git repositories, while automatic triggers respond to source code commits and base image updates. Multi-step tasks support complex build-test-push workflows entirely within ACR. Additionally, you implemented tagging and versioning strategies that support reliable production deployments. Semantic versioning communicates the nature of changes, unique tags guarantee deployment consistency, and image locking prevents accidental deletion of production images. Finally, you applied these concepts in a hands-on exercise that demonstrated building, tagging, and running container images using the Azure CLI.
 
+::: zone-end
+
 ## Additional resources
 
 - [Azure Container Registry documentation](/azure/container-registry/)