Restructured repository

README.md

@@ -1,5 +1,6 @@
-# Google Cloud AI/ML Platform Reference Architectures
+# Google Cloud Accelerated Platform Reference Architectures
 
-This repository is collection of AI/ML platform reference architectures and use cases for Google Cloud.
+This repository is collection of accelerated platform reference architectures and use cases for Google Cloud.
 
-- [GKE ML Platform for enabling ML Ops](/docs/gke-ml-platform.md)
+- [GKE AI/ML Platform for enabling AI/ML Ops](/docs/platforms/gke-aiml/README.md)
+  - [Model Fine Tuning Pipeline](/docs/use-cases/model-fine-tuning-pipeline/README.md)

docs/notebook/packaging.md → docs/guides/packaging-jupyter-notebooks/README.md

@@ -17,13 +17,13 @@ This process can also be automated utilizing Continuous Integration (CI) tools s
 
    To pair the notebook, simply use the pair function in the File menu:
 
-   ![jupyter-pairing](../images/notebook/jupyter-pairing.png)
+   ![jupyter-pairing](images/jupyter-pairing.png)
 
-   In this example we use the file [gpt-j-online.ipynb](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/ray-on-gke/examples/notebooks/gpt-j-online.ipynb):![jupyter-gpt-j-online-ipynb](/docs/images/notebook/jupyter-gpt-j-online-ipynb.png)
+   In this example we use the file [gpt-j-online.ipynb](https://github.com/GoogleCloudPlatform/ai-on-gke/blob/main/ray-on-gke/examples/notebooks/gpt-j-online.ipynb):![jupyter-gpt-j-online-ipynb](images/jupyter-gpt-j-online-ipynb.png)
 
 1. After pairing, we get the generated raw python:
 
-   ![jupyter-gpt-j-online-py](../images/notebook/jupyter-gpt-j-online-py.png)
+   ![jupyter-gpt-j-online-py](images/jupyter-gpt-j-online-py.png)
 
    **NOTE**: This conversion can also be performed via the `jupytext` cli with the following command:
 
@@ -45,7 +45,7 @@ This process can also be automated utilizing Continuous Integration (CI) tools s
 
    The following is an example output:
 
-   ![jupyter-generate-requirements](../images/notebook/jupyter-generate-requirements.png)
+   ![jupyter-generate-requirements](images/jupyter-generate-requirements.png)
    **NOTE**: (the `!cat requirements.txt` line is an example of the generated `requirements.txt`)
 
 1. Create the Dockerfile
@@ -91,4 +91,5 @@ The nbconvert tool is available inside your Jupyter notebook environment in Goog
    ```
 
    Below is an example of the commands
-   ![jupyter-nbconvert](../images/notebook/jupyter-nbconvert.png)
+
+   ![jupyter-nbconvert](images/jupyter-nbconvert.png)

docs/gke-ml-platform.md → docs/platforms/gke-aiml/README.md

@@ -4,13 +4,13 @@
 
 This reference architecture demonstrates how to build a GKE platform that facilitates Machine Learning. The reference architecture is based on the following principles:
 
-- The platform admin will create the GKE platform using IaC tool like [Terraform][terraform]. The IaC will come with re-usable modules that can be referred to create more resources as the demand grows.
-- The platform will be based on [GitOps][gitops].
-- After the GKE platform has been created, cluster scoped resources on it will be created through [Config Sync][config-sync] by the admins.
+- The platform admin will create the GKE platform using IaC tool like [Terraform](https://www.terraform.io/). The IaC will come with re-usable modules that can be referred to create more resources as the demand grows.
+- The platform will be based on [GitOps](https://about.gitlab.com/topics/gitops/).
+- After the GKE platform has been created, cluster scoped resources on it will be created through [Config Sync](https://cloud.google.com/anthos-config-management/docs/config-sync-overview) by the admins.
 - Platform admins will create a namespace per application and provide the application team member full access to it.
-- The namespace scoped resources will be created by the Application/ML teams either via [Config Sync][config-sync] or through a deployment tool like [Cloud Deploy][cloud-deploy]
+- The namespace scoped resources will be created by the Application/ML teams either via Config Sync or through a deployment tool like [Cloud Deploy](https://cloud.google.com/deploy)
 
-For an outline of products and features used in the platform, see the [Platform Products and Features](/docs/gke-ml-platform/products-and-features.md) document.
+For an outline of products and features used in the platform, see the [Platform Products and Features](products-and-features.md) document.
 
 ## Critical User Journeys (CUJs)
 
@@ -47,36 +47,24 @@ For an outline of products and features used in the platform, see the [Platform
 
 - This guide is meant to be run on [Cloud Shell](https://shell.cloud.google.com) which comes preinstalled with the [Google Cloud SDK](https://cloud.google.com/sdk) and other tools that are required to complete this tutorial.
 - Familiarity with following
-  - [Google Kubernetes Engine][gke]
-  - [Terraform][terraform]
-  - [git][git]
-  - [Google Configuration Management root-sync][root-sync]
-  - [Google Configuration Management repo-sync][repo-sync]
-  - [GitHub][github]
+  - [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine)
+  - [Terraform](https://www.terraform.io/)
+  - [git](https://git-scm.com/)
+  - [Google Configuration Management root-sync](https://cloud.google.com/anthos-config-management/docs/reference/rootsync-reposync-fields)
+  - [Google Configuration Management repo-sync](https://cloud.google.com/anthos-config-management/docs/reference/rootsync-reposync-fields)
+  - [GitHub](https://github.com/)
 
 ## Deploy the platform
 
-[Playground Reference Architecture](/examples/platform/playground/README.md): Set up an environment to familiarize yourself with the architecture and get an understanding of the concepts.
+[Playground Reference Architecture](/platforms/gke-aiml/playground/README.md): Set up an environment to familiarize yourself with the architecture and get an understanding of the concepts.
 
 ## Use cases
 
-- [Distributed Data Processing with Ray](/examples/use-case/data-processing/ray/README.md): Run a distributed data processing job using Ray.
-- [Dataset Preparation for Fine Tuning Gemma IT With Gemini Flash](/examples/use-case/data-preparation/gemma-it/README.md): Generate prompts for fine tuning Gemma Instruction Tuned model with Vertex AI Gemini Flash
-- [Fine Tuning Gemma2 9B IT model With FSDP](/examples/use-case/fine-tuning/pytorch/README.md): Fine tune Gemma2 9B IT model with PyTorch FSDP
+- [Model Fine Tuning Pipeline](/docs/use-cases/model-fine-tuning-pipeline/README.md)
+  - [Distributed Data Processing with Ray](/use-cases/model-fine-tuning-pipeline/data-processing/ray/README.md): Run a distributed data processing job using Ray.
+  - [Dataset Preparation for Fine Tuning Gemma IT With Gemini Flash](/use-cases/model-fine-tuning-pipeline/data-preparation/gemma-it/README.md): Generate prompts for fine tuning Gemma Instruction Tuned model with Vertex AI Gemini Flash
+  - [Fine Tuning Gemma2 9B IT model With FSDP](/use-cases/model-fine-tuning-pipeline/fine-tuning/pytorch/README.md): Fine tune Gemma2 9B IT model with PyTorch FSDP
 
 ## Resources
 
-- [Packaging Jupyter notebooks](/docs/notebook/packaging.md): Patterns and tools to get your ipynb's ready for deployment in a container runtime.
-
-[gitops]: https://about.gitlab.com/topics/gitops/
-[repo-sync]: https://cloud.google.com/anthos-config-management/docs/reference/rootsync-reposync-fields
-[root-sync]: https://cloud.google.com/anthos-config-management/docs/reference/rootsync-reposync-fields
-[config-sync]: https://cloud.google.com/anthos-config-management/docs/config-sync-overview
-[cloud-deploy]: https://cloud.google.com/deploy?hl=en
-[terraform]: https://www.terraform.io/
-[gke]: https://cloud.google.com/kubernetes-engine?hl=en
-[git]: https://git-scm.com/
-[github]: https://github.com/
-[gcp-project]: https://cloud.google.com/resource-manager/docs/creating-managing-projects
-[personal-access-token]: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens
-[machine-user-account]: https://docs.github.com/en/get-started/learning-about-github/types-of-github-accounts
+- [Packaging Jupyter notebooks](/docs/guides/packaging-jupyter-notebooks/README.md): Patterns and tools to get your ipynb's ready for deployment in a container runtime.

docs/gke-ml-platform/playground/architecture.md → docs/platforms/gke-aiml/playground/architecture.md

@@ -1,6 +1,6 @@
 # Playground Machine learning platform (MLP) on GKE: Architecture
 
-![Playground Architecture](/docs/images/platform/playground/mlp_playground_architecture.svg)
+![Playground Architecture](/docs/platforms/gke-aiml/playground/images/architecture.svg)
 
 ## Platform
 
@@ -20,7 +20,7 @@
     - CPU system node pool
     - GPU on-demand node pool
     - GPU spot node pool
-- Google Kubernetes Engine (GKE) Enterprise ([docs])(https://cloud.google.com/kubernetes-engine/enterprise/docs)
+- [Google Kubernetes Engine (GKE) Enterprise](https://cloud.google.com/kubernetes-engine/enterprise/docs)
   - Configuration Management
     - Config Sync
     - Policy Controller
@@ -38,7 +38,7 @@
 - [Classic SSL Certificate](https://console.cloud.google.com/security/ccm/list/lbCertificates)
   - Gateway SSL Certificate
     - Ray dashboard
-- Identity-Aware Proxy (IAP) ([docs])(https://cloud.google.com/iap/docs/concepts-overview)
+- [Identity-Aware Proxy (IAP)](https://cloud.google.com/iap/docs/concepts-overview)
   - Ray head Backend Service
 - [Service Accounts](https://console.cloud.google.com/iam-admin/serviceaccount)
   - Default