Zopa uses SageMaker MMS model serving stack in a similar BYOC fashion to register the models for the SageMaker Clarify processing job. Zopa trains its fraud detection model on SageMaker and can use SageMaker Clarify to view a feature attributions plot in SageMaker Experiments after the model has been trained.Īs previously mentioned, model explanations are carried out both during model training for model validation and after deployment for model monitoring and generating insights for underwriters. In this role, you will get an opportunity to work backward from our customers and collaborate with product and science teams to take cutting edge. One of the key factors why Zopa chose SageMaker Clarify was due to the benefit of a fully managed service for model explanations with pay-as-you-go billing and the integration with the training and deployment phases of SageMaker. Host a machine learning model in Amazon SageMaker and capture inference requests, results, and metadata Schedule Clarify bias monitor to monitor predictions. We are on mission to build on existing capabilities of Amazon SageMaker Clarify to invent new techniques to explain machine learning models, and to detect and mitigate bias in data and models.
The data scientists at Zopa often used several traditional feature importance methods to understand the impact of the input features in non-linear ML models, such as the Partial Dependence Plots and Permutation Feature Importance. Amazon SageMaker Clarify provides ML developers with greater visibility into their training data and models so they can identify and limit bias and explain. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58. Examples include using CSV and JSON Lines data formats, bringing your own container, and running processing jobs with Spark. One of the key factors why Zopa chose SageMaker Clarify was due to the benefit of a fully managed service for Amazon SageMaker Clarify Processing Use SageMaker Clarify to create a processing job for the detecting bias and explaining model predictions with feature attributions. The data scientists at Zopa often used several traditional feature importance methods to understand the impact of the input features in non-linear ML models, such as the Partial Dependence Plots and Permutation Feature Importance.
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