Different artifact type names across execution environments

If the bug is related to a specific library below, please raise an issue in the
respective repo directly:

TensorFlow Data Validation Repo

TensorFlow Model Analysis Repo

TensorFlow Transform Repo

TensorFlow Serving Repo

System information

• Have I specified the code to reproduce the issue (Yes, No): No
• Environment in which the code is executed (e.g., Local(Linux/MacOS/Windows), - Linux local + Kubernetes
  Interactive Notebook, Google Cloud, etc): Bare metal
• TensorFlow version: 2.15
• TFX Version: 2.16 dev
• Python version: 3.10.14
• Python dependencies (from pip freeze output): ml-metadata==1.15.0

Describe the current behavior
When producing an example artifact locally, the "type" is Examples. When producing the same thing where Kubeflow pipelines is the runner, the artifact type is tfx.Examples. This makes the usage of TFX more brittle.

Describe the expected behavior
Artifact types should be consistent across environments

Standalone code to reproduce the issue
Write a TFX pipeline, compile it to run on kfp v2, and look at what's in ml_metadata.

Providing a bare minimum test case or step(s) to reproduce the problem will
greatly help us to debug the issue. If possible, please share a link to
Colab/Jupyter/any notebook.

Name of your Organization (Optional)
Intuitive.ai

Other info / logs

Include any logs or source code that would be helpful to diagnose the problem.
If including tracebacks, please include the full traceback. Large logs and files
should be attached.