Implement the predict() function for SageMaker deployment plugin

[jamestran201]

Signed-off-by: James Tran tran.james2001@gmail.com

What changes are proposed in this pull request?

Implement the predict() function for SageMaker deployment plugin. A prediction can be obtained through Python code or mlflow deployments predict command. For example:

cat > ./input.json <<- input
{"feat1": {"0": 1}, "feat2": {"0": 2}, "feat3": {"0": 3}}
input

mlflow deployments predict \\
    --target sagemaker:/us-east-1/arn:aws:1234:role/assumed_role \\
    --name my-deployment \\
    --input-path ./input.json

How is this patch tested?

I have added a unit test and tested the CLI manually using a scikit-learn model.

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.

Implement the predict() function for SageMaker deployment plugin. A prediction can be obtained through Python code or mlflow deployments predict command.

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

[jamestran201]

@dbczumar Could you please review this PR when you have time?

I have tested getting a prediction from a scikit-learn model. I haven't tried with other frameworks such as Tensorflow. Do you know if the response format from SageMaker is the same across different frameworks?

[dbczumar]

LGTM! Thanks @jamestran201 ! I pushed a couple small tweaks to support list, numpy array, etc. types for input / output. These aren't currently supported by the mlflow deployments predict CLI (see #5084), but they are supported by the API.

[jamestran201]

@dbczumar Thanks!