Uncover Model Risk Governance in Machine Learning Operations

Explore the forefront of Machine Learning Operations and Model Risk Governance

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Join us for an insightful webinar exploring the critical intersection of Machine Learning Operations (MLOps) and model risk governance. Discover how MLOps practices play a pivotal role in managing model risk, ensuring transparency, reliability, and compliance throughout the machine learning lifecycle.

From model validation to performance monitoring, delve into the essential components of effective model risk governance within MLOps frameworks. Gain valuable insights into mitigating model bias, understanding model interpretability, and maintaining regulatory compliance in an ever-evolving landscape.

Don't miss out on this opportunity to explore the forefront of MLOps and model risk governance. Join us and empower your organisation to navigate the complexities of model risk with confidence and resilience!

What to expect

  • Introduction to MLOps and Model Lifecycle Management.
  • Importance of governance and reducing model risk of machine learning models with MLOps.
  • Regulatory landscape and compliance challenges surrounding machine learning models in various industries.
  • Model monitoring and performance monitoring in production environment.
  • Case study: Credit rating machine learning model solution in a MLOps workflow.

Who should attend?

  • Heads of Model Development, Model Risk, AI, Data Analytics, Advanced Analytics, MLOps
  • Machine learning engineers
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About the Presenter
About the Presenters

Leo Breedt is a senior consultant who works with customers to assist them to implement model lifecycle management solutions, ensuring a smooth transition from model development to production with a strong emphasis on model governance which include model risk management practices.

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