In the realm of artificial intelligence (AI), the concept of AI governance at scale is gaining significant attention. Trustible, a company that empowers organizations to responsibly build, deploy, and monitor AI systems at scale, has recently announced its partnership with Databricks. This collaboration aims to combine Trustible’s leading AI governance platform with Databricks’ trusted data and AI lakehouse.
Trustible’s Mission
Trustible’s mission is to enable organizations to maximize the benefits of AI while minimizing its risks. The company believes that AI practitioners and innovators should focus on both these aspects. Their expertise and knowledge of the systems will be necessary to comply with emerging regulations and maintain a corpus of documentation evidence required for compliance.
The Role of AI Governance
AI governance plays a crucial role in translating complex legal requirements and responsible AI frameworks into actionable steps. This enables collaboration between AI and legal/compliance teams, ensuring that AI systems are not only effective but also compliant with relevant regulations.
The Pain Point that Led to the Partnership
The partnership between Trustible and Databricks emerged from a pain point that the Trustible team experienced while leading AI/ML teams. The challenge was leveraging information already stored in the Databricks Lakehouse to accelerate compliance with emerging regulations like the European Union’s AI Act. The team also wanted to set up policies and processes that are prepared for future requirements such as external audits, post-market monitoring, and public disclosure requirements.
The Role of Documentation in AI Governance
Emerging AI regulations like the EU AI Act require extensive documentation and disclosure about underlying models. Key model attributes such as training objectives, accuracy metrics, and bias/fairness statistics must be provided to users and regulators. This is to properly convey key risks, limitations, and mitigation steps. Storing these kinds of metrics and metadata in a model registry such as MLflow is considered best practice and will soon be a regulatory requirement.
Trustible’s Integration with MLflow on Databricks
Trustible’s integration with MLflow on Databricks allows the platform to seamlessly generate regulatory model documentation. It does this by automatically mapping MLflow metrics and metadata to required fields in Model Cards, tailoring reporting to legal and governance needs. This integration is set to extend across the full machine learning lifecycle, empowering continuous monitoring, auditing, and transparency as regulations and customer needs expand.
The Future of AI Development
The future of AI development will require visibility and collaboration between a broader set of stakeholders such as compliance teams, senior management, regulators, and the broader public. Trustible enables organizations to build trusted and accountable AI systems by connecting the needs and requirements of these various stakeholder groups.
Conclusion
Trustible’s partnership with Databricks represents a significant step forward in AI governance at scale. By combining Trustible’s leading AI governance platform with Databricks’ trusted data and AI lakehouse, the partnership is set to redefine the future of AI development, ensuring that it is not only effective but also responsible and compliant.
FAQs
1. What is Trustible’s mission?
Trustible’s mission is to empower organizations to responsibly build, deploy, and monitor AI systems at scale. The company aims to maximize the benefits of AI while minimizing its risks.
2. What led to the partnership between Trustible and Databricks?
Are you interested in AI, business and tech? Receive weekly actionable tips and advice right in your inbox. Work less, earn more and read about the latest trends before everyone else 🫵
The partnership emerged from a pain point that the Trustible team experienced while leading AI/ML teams. The challenge was leveraging information already stored in the Databricks Lakehouse to accelerate compliance with emerging regulations.
3. How does Trustible’s integration with MLflow on Databricks work?
Trustible’s integration with MLflow on Databricks allows the platform to seamlessly generate regulatory model documentation by automatically mapping MLflow metrics and metadata to required fields in Model Cards.
4. What is the future of AI development according to Trustible?
The future of AI development will require visibility and collaboration between a broader set of stakeholders such as compliance teams, senior management, regulators, and the broader public. Trustible aims to enable organizations to build trusted and accountable AI systems by connecting the needs and requirements of these various stakeholder groups.
5. What does the partnership between Trustible and Databricks mean for AI governance at scale?
The partnership represents a significant step forward in AI governance at scale. It combines Trustible’s leading AI governance platform with Databricks’ trusted data and AI lakehouse, redefining the future of AI development to ensure it is not only effective but also responsible and compliant.
