Kf 4301 charmed vs upstream and KF-4298 Contributing (#203)

* WIP

* Fixes

* Updates

* Spelling

* Contributing

* Docs updates

CONTRIBUTING.md

@@ -1,13 +1,15 @@
 # Contributing
 
+<!-- Include start contributing -->
+
 ## Overview
 
 This document outlines the processes and practices recommended for contributing enhancements to
 this operator.
 
 ## Talk to us First
 
-Before developing enhancements to this charm, you should [open an issue](/../../issues) explaining your use case. If you would like to chat with us about your use-cases or proposed implementation, you can reach us at [MLOps Mattermost public channel](https://chat.charmhub.io/charmhub/channels/mlops-documentation) or on [Discourse](https://discourse.charmhub.io/).
+Before developing enhancements to this charm, you should [open an issue](https://github.com/canonical/mlflow-operator/issues) explaining your use case. If you would like to chat with us about your use-cases or proposed implementation, you can reach us at [MLOps Mattermost public channel](https://chat.charmhub.io/charmhub/channels/mlops-documentation) or on [Discourse](https://discourse.charmhub.io/).
 
 ## Pull Requests
 
@@ -73,3 +75,5 @@ To upgrade the source and resources of this charm, you must:
 ## Canonical Contributor Agreement
 
 Canonical welcomes contributions to this charm. Please check out our [contributor agreement](https://ubuntu.com/legal/contributors) if you're interested in contributing.
+
+<!-- Include end contributing -->

docs/.wordlist.txt

@@ -4,6 +4,8 @@ APIs
 balancer
 CharmHub
 CLI
+CNCF
+CVEs
 DEX
 Diátaxis
 dropdown
@@ -20,10 +22,12 @@ JSON
 Juju
 Jupyter
 JupyterLab
+KServe
 Kubeflow
 Kubernetes
 lifecycle
 Makefile
+Mattermost
 MicroK
 MinIO
 MLflow
@@ -38,6 +42,7 @@ OLM
 Permalink
 ReadMe
 readthedocs
+rebasing
 reproducibility
 reST
 reStructuredText
@@ -48,3 +53,4 @@ UI
 URI
 TensorBoard
 VM
+YAML

docs/contributing.md

@@ -0,0 +1,7 @@
+# Contribute to MLflow
+
+% Include content from [../CONTRIBUTING.md](../CONTRIBUTING.md)
+```{include} ../CONTRIBUTING.md
+    :start-after: <!-- Include start contributing -->
+    :end-before: <!-- Include end contributing -->
+```

docs/explanation/index.rst

@@ -1,4 +1,7 @@
 Explanation
 ===========
 
-Coming soon.
+.. toctree::
+   :maxdepth: 1
+
+   why-charmed

docs/explanation/why-charmed.rst

@@ -0,0 +1,47 @@
+Why choose charmed MLflow?
+==========================
+
+Are you considering using Charmed MLflow? Wondering what the advantages are of charmed MLflow vs. upstream `MLflow <https://mlflow.org/>`_?
+
+Knowing the answer to this will help any prospective MLflow users decide whether they want the charmed version.
+
+Simplified deployment
+--------------------------------------------
+
+Charmed MLflow offers simplified deployment. Like any charmed product, Charmed MLflow is deployed as a `charm bundle <https://juju.is/docs/juju/bundle>`_ using `Juju <https://juju.is/>`_. Deploying an application with Juju is arguably simpler than deploying to a raw Kubernetes cluster.
+
+Security, stability, and maintenance
+-------------------------------------
+
+Charmed MLflow benefits from the following:
+
+- Upgrade guides.
+- Automated security scanning: The bundle is scanned periodically.
+- Security patching: Charmed MLflow follows Canonical’s process and procedure for security patching. Vulnerabilities are prioritised based on severity, the presence of patches in the upstream project, and the risk of exploitation.
+- Maintained images: All Charmed MLflow images are actively maintained.
+- Comprehensive testing: Charmed MLflow is thoroughly tested on multiple platforms, including public cloud, local workstations, on-premises deployments, and various CNCF-compliant Kubernetes distributions.
+
+Integration
+-----------
+
+Charmed MLflow provides integration capabilities, including:
+
+- Customised Prometheus exporter metrics
+- Customised MLflow dashboard for Grafana
+- Canonical Observability Stack
+- Charmed Kubeflow: including the ability use the MLflow registry directly from Kubeflow pipelines and notebooks
+
+Enterprise Offering
+--------------------
+
+Charmed MLflow offers an enterprise offering from Canonical, which includes:
+
+- 24/7 support for deployment, up-time monitoring, and security patching with Charmed MLflow.
+- Hardening features and compliance with standards like Federal Risk and Authorisation Management Program, Health Insurance Portability and Accountability Act, and Payment Card Industry Digital Signature Standard, making it suitable for enterprises running AI/ML workloads in highly regulated environments.
+- Timely patches for common vulnerabilities and exposures (CVEs).
+- A ten-year security maintenance commitment.
+- Hybrid cloud and multi-cloud support.
+- Bug fixing.
+- Optionally managed services, allowing your team to focus on development rather than operations.
+- Consultancy services to assess the best tools and architecture for your specific use cases.
+- A simple per-node subscription model.

docs/index.rst

@@ -59,4 +59,5 @@ Charmed MLflow is an open-source project that values its community. We warmly we
    tutorial/index
    how-to/index
    reference/index
-   explanation/index
+   explanation/index
+   contributing