Implement MLTransform One-Hot Encoding benchmark pipeline

[aIbrahiim]

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Summary of Changes

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This pull request introduces a new benchmark pipeline for categorical feature encoding using Apache Beam's MLTransform. The changes include the pipeline implementation, supporting test suites, and the necessary infrastructure to integrate performance tracking and cost estimation into the Beam website's performance dashboard.

Highlights

• New MLTransform Pipeline: Implemented a categorical encoding pipeline using MLTransform's ComputeAndApplyVocabulary transform to convert categorical features into integer indices.
• Testing and Benchmarking: Added comprehensive unit and integration tests, along with a new Dataflow benchmark to track throughput, latency, and cost metrics.
• Documentation and Metrics: Updated the project website to include a new performance dashboard page and configured Looker metrics for the new benchmark.

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• Ignored by pattern: .github/workflows/** (2)
  • .github/workflows/beam_Inference_Python_Benchmarks_Dataflow.yml
  • .github/workflows/load-tests-pipeline-options/beam_Inference_Python_Benchmarks_Dataflow_MLTransform_One_Hot_Encoding_Batch.txt

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Code Review

This pull request introduces a new example and benchmark for MLTransform One-Hot Encoding in the Python SDK, including performance tracking and documentation updates. Review feedback points out several issues: unit tests for a non-existent function that cause failures, the need for a filter to handle missing columns and prevent pipeline crashes, and opportunities to optimize synthetic data generation and improve logging for text-based inputs.

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