Adding ML platform reference architecture (#266)

Co-authored-by: Aaron Rueth <[email protected]>
Co-authored-by: Kent Hua <[email protected]>
Co-authored-by: Kavitha Rajendran <[email protected]>

best-practices/ml-platform/README.md

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+# Machine learning platform (MLP) on GKE reference architecture for enabling Machine Learning Operations (MLOps)
+
+## Platform Principles
+
+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.
+- 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]
+
+## Critical User Journeys (CUJs)
+
+### Persona : Platform Admin
+
+- Offer a platform that incorporates established best practices.
+- Grant end users the essential resources, guided by the principle of least privilege, empowering them to manage and maintain their workloads.
+- Establish secure channels for end users to interact seamlessly with the platform.
+- Empower the enforcement of robust security policies across the platform.
+
+### Persona : Machine Learning Engineer
+
+- Deploy the model with ease and make the endpoints available only to the intended audience
+- Continuously monitor the model performance and resource utilization
+- Troubleshoot any performance or integration issues
+- Ability to version, store and access the models and model artifacts:
+  - To debug & troubleshoot in production and track back to the specific model version & associated training data
+  - To quick & controlled rollback to a previous, more stable version
+- Implement the feedback loop to adapt to changing data & business needs:
+  - Ability to retrain / fine-tune the model.
+  - Ability to split the traffic between models (A/B testing)
+  - Switching between the models without breaking inference system for the end-users
+- Ability to scaling up/down the infra to accommodate changing needs
+- Ability to share the insights and findings with stakeholders to take data-driven decisions
+
+### Persona : Machine Learning Operator
+
+- Provide and maintain software required by the end users of the platform.
+- Operationalize experimental workload by providing guidance and best practices for running the workload on the platform.
+- Deploy the workloads on the platform.
+- Assist with enabling observability and monitoring for the workloads to ensure smooth operations.
+
+## Prerequisites
+
+- 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]
+
+## Deploy the platform
+
+[Sandbox Reference Architecture Guide](examples/platform/sandbox/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/ray/dataprocessing/README.md): Run a distributed data processing job using Ray.
+
+[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