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Future Development

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New Domain-Specific Toolkits

We plan to continue expanding the framework by releasing engines tailored to other types of ML experiments:

  • synthetic tabular time-series generation,
  • synthetic image generation,
  • text generation,
  • multiclass classification.

These extensions will maintain the same core philosophy of decoupling validation logic from model implementation, allowing researchers to focus on innovation while ensuring consistent evaluation practices.

Enhanced Integration

Future versions will provide deeper integration with popular ML frameworks and tools:

  • integration with other machine learning frameworks (e.g. TensorFlow),
  • support for distributed validation on large datasets (e.g. integration with PySpark).

Performance Optimization

  • running validation asynchronously in the background via a distributed task manager.

Relevant Pages

For detailed contribution guidelines please consult the relevant docs.

For detailed PR guidelines please consult the relevant docs.