Continuous Metalearning

Continuous Metalearning enables:

• Management of model performance, ensuring that it remains as performant, or more performant than it was at the point of deployment.
• Assure that fairness and bias metrics are still met in production, given the changes that can happen with new data inputs.
• The ability for your model to learn new, distinct, yet relevant intelligence in production by utilising meta-learning techniques.
• A centralised history of your models, including data provenance, enabling proper audits and a full chain of accountability for model behaviour.

Benefits of Continuous Metalearning:

• Assurance that an evolving, in-production model remains responsible.
• Continuous monitoring of all models to ensure that performance remains consistent, or improves, with safeguards built to protect against performance degradation.
• Automatic updates when new trends are detected.
• Continuous support of models in production.
• Explainability and transparency of the origins and subsequent operations of all the models in production, including lineage and provenance of the data and models.

How it works

Continuous Metalearning spans the post-production lifecycle of a model and ensures responsible AI governance across your entire portfolio.

"We get excited when we see the results of our models in production, but it doesn't stop there. The models need to continue to be trained, enhanced and tested to ensure they’re not biased, and that they continue to predict with the level of accuracy that we expect. Continuous Metalearning is really critical for any business and for anyone using AI and machine learning."

Nika Lee
Chief Underwriting Officer

Learn how Aioi Nissay Dowa Europe are utilising the power of Continuous Metalearning to combat fraud - Read the case study.

Why do you need Continuous Metalearning?

There is a lot of emphasis on ensuring that AI models are designed and developed responsibly, with many toolkits and ethical design processes related to these specific stages of the lifecycle. There is less support to ensure it continues to behave responsibly, once a model has been deployed. This is critical, as AI and the space in which AI operates, change over time. Continuous Metalearning addresses continual monitoring, continuous improvement, and builds transparency into the origins of the model. These principles are central to ensuring an evolving, in-production model remains responsible.