Trustible's integration with MLflow on Databricks generates regulatory model documentation by mapping MLflow metrics and metadata to required fields in Model Cards. This tailors reporting to legal and governance needs and ensures that models reaching production have the necessary documentation. The platform will also extend integrations across the full machine learning lifecycle, enabling continuous monitoring, auditing, and transparency as regulations and customer needs expand.
Key takeaways:
- Trustible has announced a partnership with Databricks to combine their AI governance platform with Databricks' data and AI lakehouse, aiming to help customers implement responsible, compliant, and accountable AI.
- Trustible's platform translates complex legal requirements and responsible AI frameworks into actionable steps, enabling collaboration between AI and legal/compliance teams.
- Trustible's integration with Databricks and MLflow aims to generate regulatory model documentation by automatically mapping MLflow metrics and metadata to required fields in Model Cards, tailoring reporting to legal and governance needs.
- Trustible plans to extend integrations across the full machine learning lifecycle, empowering continuous monitoring, auditing, and transparency as regulations and customer needs expand.