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MLOps.community

[Exclusive] Weights & Biases Round-table // Model Management in a Regulated Environment

Dec 15, 2023
58:29
Snipd AI
Join the Weights & Biases round-table discussion on model management in regulated environments. Explore challenges in compliance and effective strategies. Learn about tracking and documenting the model's journey, avoiding undesirable outcomes, open source LLMs, challenges of using Weights & Biases in finance, E-vowels as a service, organization structure, and the value of expertise.
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Podcast summary created with Snipd AI

Quick takeaways

  • Model management in regulated environments requires transparency, compliance, and collaboration with various stakeholders.
  • Establishing effective collaboration between data science teams and other departments is crucial for successful implementation of machine learning projects in regulated environments.

Deep dives

Effective model management in regulated environments

Model management in regulated environments involves the entire model lifecycle, from data collection to deployment and ongoing monitoring. It requires managing model checkpoints and ensuring compliance with audits. For financial services, auditability and justifying forecasted values are key. Transparency and explainability are important to build trust with stakeholders and the regulatory bodies. Customized evaluation metrics and storytelling can help communicate model insights and limitations to non-technical stakeholders. Collaboration between data science teams and other departments is crucial, whether through embedding team members from different disciplines or having dedicated stakeholder management and governance roles.

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