Push MLflow model to Sagemaker model registry

[jinnig]

What changes are proposed in this pull request?

This PR proposed the solutions to #4658 and #4667.

• Implemented the push_model_to_sagemaker() API to push a model from MLflow to Sagemaker model registry.
• Built the CLI support for the API.
• Updated https:// protocol with s3:// protocol to upload Sagemaker model data to S3.

How is this patch tested?

• Manually tested both push_model_to_sagemaker() API and the CLI.
• Executed all unit tests, including the tests for the existing private create_sagemaker_model method.
• Manually reviewed the python doc.

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.

(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)

• Create a new push_model_to_sagemaker() API and CLI to push MLflow model to Sagemaker model registry.
• Updated https:// protocol with s3:// protocol to upload Sagemaker model data to S3.

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

[jinnig]

Hey @dbczumar, could you please take a look at this PR when you have time? Thank you!

[YQ-Wang]

Hey @dbczumar, please let us know if anything else needs to be done for this PR. Thanks!

[dbczumar]

Hi @YQ-Wang @jinnig Thank you for this contribution, we'll review it shortly!

[jinzhang21]

LGTM overall. Triggered a test to see if it passes the command test run. I'll approve after the last comment addressed.

[jinzhang21]

LGTM. Could you please run black and lint.sh to fix the lint issue? I also saw a test failure during the last round of review. Not sure if it's fixed since. Please verify mlflow/mlflow/tests/examples/test_examples.py passes (also run dev/run-small-python-tests.sh and dev/run-large-python-tests.sh to check for other potential failures).