Return Model from log_model

[liangz1]

What changes are proposed in this pull request?

Provide a return value from mlflow.*.log_model() that contains model metadata such as model_uri, run_id,...
This metadata makes it easier for the user to do follow-up model loading.

How is this patch tested?

Existing tests + tests/models/test_model.py test_model_info()

Does this PR change the documentation?

• No. You can skip the rest of this section.
• Yes. Make sure the changed pages / sections render correctly by following the steps below.

1. Check the status of the ci/circleci: build_doc check. If it's successful, proceed to the
   next step, otherwise fix it.
2. Click Details on the right to open the job page of CircleCI.
3. Click the Artifacts tab.
4. Click docs/build/html/index.html.
5. Find the changed pages / sections and make sure they render correctly.

Release Notes

Is this a user-facing change?

• No. You can skip the rest of this section.
• Yes. Give a description of this change to be included in the release notes for MLflow users.

mlflow.*.log_model() returns a ModelInfo instance that contains the metadata of the logged model.

What component(s), interfaces, languages, and integrations does this PR 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

Interface

• 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

Language

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

• rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
• rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
• rn/feature - A new user-facing feature worth mentioning in the release notes
• rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
• rn/documentation - A user-facing documentation change worth mentioning in the release notes

[dbczumar]

LGTM once all model flavors are updated!

[harupy]

LGTM once #5230 (comment) is addressed.

[liangz1]

@harupy Also,

:py:class:`ModelInfo <mlflow.models.model.ModelInfo>`

works while the following does not:

:py:class:`ModelInfo`

Thanks for helping!

[harupy]

Suggested change

	signature: ModelSignature
	signature: Optional[ModelSignature]

Can we use Optional for properties that can be None?

[liangz1]

I changed it to a native python dictionary that is ModelSignature.to_dict() to avoid importing pandas in mlflow skinny. Another reason is that for the user's information, there is no need to return the ModelSignature object. A dictionary should be sufficient for the purpose of information reading.

[liangz1]

I added example values for some attributes.

[liangz1]

Improve the Model class property docstring.