Using Azure Machine Learning Notebooks for Quality Control of Automated Predictive Pipelines
When building an automated predictive pipeline, to have periodically batch-wise score new data, there is a need to control for quality of the predictions. The Azure Data Factory (ADF) pipeline will help you ensure that your whole data set gets scored. However, this is not taking into consideration that data can change over time. For example, when predicting churn changes in your website or service offerings could change customer behavior in such a way that retraining of the original model is needed. In this blog post I show how you can use #Jupyter Notebooks in +Microsoft Azure Machine Learning (AML) to get a more systematic view on the (predictive) performance of your automated predictive pipelines.
#DataScience #MachineLearning #Azure #AzureDataFactory #Python #Notebook
Using Azure Machine Learning Notebooks for Quality Control of Automated Predictive Pipelines – Developing Analytics Solutions with the Data Insights Global Practice – Site Home – MSDN Blogs
By Sander Timmer, PhD, Data Scientist. When building an automated predictive pipeline to have periodically batch-wise score new data there is a need to control for quality of the predictions. The Azure Data Factory (ADF) pipeline will help you ensure that your whole data set gets scored.