[FR] [Roadmap] Inline examples of model flavor usage with MLflow

MLflow Roadmap Item

This is an MLflow Roadmap item that has been prioritized by the MLflow maintainers. We’ve identified this feature as a highly requested addition to the MLflow package based on community feedback. We’re seeking a community contribution for the implementation of this feature and will enthusiastically support the development and review of a submitted PR for this.

Contribution Note

As with other roadmap items, there may be a desire for multiple contributors to work on an issue. While we don’t discourage collaboration, we strongly encourage that a primary contributor is assigned to roadmap issues to simplify the merging process. The items on the roadmap are of a high priority. Due to the wide-spread demand of roadmap features, we encourage potential contributors to only agree to take on the work of creating a PR, making changes, and ensuring that test coverage is adequately created for the feature if they are willing and able to see the implementation through to a merged state.

Feature scope

This roadmap feature’s complexity is classified as:

good-first-issue: This feature is limited in complexity and effort required to implement.
simple: This feature does not require a large amount of effort to implement and / or is clear enough to not need a design discussion with maintainers.
involved: This feature will require a substantial amount of development effort but does not require an agreed-upon design from the maintainers. The feedback given during the PR phase may be involved and necessitate multiple iterations before approval. (Please bear with us as we collaborate with you to make a great contribution)
design-recommended: This is a substantial feature that should have a design document approved prior to working on an implementation (to save your time, not ours). After agreeing to work on this feature, a maintainer will be assigned to support you throughout the development process.

Proposal Summary

This meta-FR covers the conversion of model flavors documentation to be consistent with the new, more user-friendly design of Pmdarima](https://mlflow.org/docs/latest/models.html#pmdarima-pmdarima-experimental), Model Evaluation and Diviner.

If taking on one of the below listed flavors, please request assignment below and we will tag you to that flavor’s implementation.

* pyfunc
* crate (R function)
* h2o
* keras #6781
* MLeap
* pytorch
* scikit-learn #6694
* SparkML
* Tensorflow
* ONNX
* gluon
* XGBoost
* LightGBM
* CatBoost
* SpaCy
* Fastai
* Statsmodels (linear model and timeseries model usage)
* Prophet

Motivation

What is the use case for this feature?

Make it easier to understand common usage patterns for each of these officially supported libraries within MLflow

Why is this use case valuable to support for MLflow users in general?

Reduce the complexity of figuring out the API patterns by hunting in the repository and using trial and error.

What component(s), interfaces, languages, and integrations does this feature affect?

Components

area/artifacts: Artifact stores and artifact logging
area/build: Build and test infrastructure for MLflow
area/docs: MLflow documentation pages
area/examples: Example code
area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
area/models: MLmodel format, model serialization/deserialization, flavors
area/projects: MLproject format, project running backends
area/scoring: MLflow Model server, model deployment tools, Spark UDFs
area/server-infra: MLflow Tracking server backend
area/tracking: Tracking Service, tracking client APIs, autologging

Interfaces

area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
area/docker: Docker use across MLflow’s components, such as MLflow Projects and MLflow Models
area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
area/windows: Windows support

Languages