Adding log table to log dictionary or df as artifacts in MLflow run

[sunishsheth2009]

What changes are proposed in this pull request?

Adding log table to log dictionary or df as artifacts in MLflow run

How is this patch tested?

• Add tests
• Notebook

Does this PR change the documentation?

• No. You can skip the rest of this section.
• Yes. Make sure the changed pages / sections render correctly in the documentation preview.

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.

Adding log table to log dictionary or df as artifacts in MLflow run

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/recipes: Recipes, Recipe APIs, Recipe configs, Recipe 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
• 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]

I think we can remove this warning for now. Saving thousands, even hundreds of thousands, of lines of CSV is likely fine performance wise

[dbczumar]

@sunishsheth2009 @hubertzub-db regardless, we'll want to think about memory usage in the browser when we load these artifacts, right? Is there an approach we can take for memory safety - e.g. paginating artifact downloads?

If an artifact is too large, we may want to skip it and show a warning with a list of run IDs for which table data could not be rendered

[mlflow-automation]

Documentation preview for 3fdac6f will be available here when this CircleCI job completes successfully.

More info

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• This comment was created by https://github.com/mlflow/mlflow/actions/runs/5053457216.

[harupy]

Looks good to me once the remaning comments are addressed!