Add data capture config to SageMaker Deployments

[jonwiggins]

Related Issues/PRs

None

What changes are proposed in this pull request?

This adds data_capture_config as a mostly pass-through variable such that users can these settings when deploying models to AWS SageMaker.

Let me know if you need me to make any changes.

How is this patch tested?

I deployed a few models using this patch, and saw that the data capture configs had been set.

Does this PR change the documentation?

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

1. Click the Details link on the Preview docs check.
2. 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.
• [X] Yes. Give a description of this change to be included in the release notes for MLflow users.

This adds data_capture_config as a parameter in the SageMaker deployment functions such that users can these settings when deploying models to AWS SageMaker.

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/pipelines: Pipelines, Pipeline APIs, Pipeline configs, Pipeline Templates
• [ ] 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
• [X] integrations/sagemaker: SageMaker integrations
• [ ] integrations/databricks: Databricks integrations

Notes

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
• [X] 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

[github-actions[bot]]

@jonwiggins Thanks for the contribution! The DCO check failed. Please sign off your commits by following the instructions here: https://github.com/mlflow/mlflow/runs/7734300361. See https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.rst#sign-your-work for more details.

[jonwiggins]

@dbczumar Great thanks.

I tried to make the changes you requested. Let me know what you think. Happy to make any needed changed.