Generic framework for Azure machine Learning AutoML - Hyper parameter, featurization techniques

Question

Currently we have implemented multiple Insurance use cases(Claims, Policy) using AutoML in Azure Machine Learning and created real-time endpoints.
We have a standard re-usable python scripts available where with few configuration changes, we are reusing this script for multiple use cases and quickly develop endpoints,

Currently, we need to apply our Insurance domain knowledge and enrich the training data set.
To do this, We understand there are features like Hyper parameter tuning, featurization, encoding techniques, etc. We understand that there are python libraries for that, but is there a generic framework/coding available so that we can make use of this and implement across multiple use cases to increase the model accuracy. This is mainly to reduce the dependency on data scientist and reduce the azure ml implementation time

Answer 1

@Rajavarman Thanks for the question. Previously, it was a black-box preprocessing, with user’s preprocess=True/False setting.
New change includes deprecation of preprocess and introduction of new field featurization, where featurization = ‘auto’ (for automatic featurization, comparable to preprocess=True) / ‘off’ (to turn off featurization, comparable to preprocess=False) / FeaturizationConfig (object to pass in customized configuration on featurization setting).
For more information on custom featurization as well as how to construct FeaturizationConfig is in this documentation: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train#customize-feature-engineering
We also have a notebook available with example in our git repo: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb